Innovative Application and Development Trend of Algorithm
Platform in Textile Design
Yinan Sun
Beijing Institute of Fashion Technology, 100029, China
Keywords: Comprehensive Application Theory, Algorithm Platform, Textile Design, Innovative Applications, Trends.
Abstract: In view of the limitations of traditional image recognition algorithms in the innovative application and
development trend of textile design, an innovative application and development trend scheme in the design
based on the algorithm platform is proposed. Firstly, the influencing factors is accurately located through the
comprehensive use of theory, and the indicators is reasonably divided to reduce interference, and the algorithm
platform is used to construct innovative applications and development trends in the design. Experimental
results show that under certain evaluation criteria, the proposed scheme is superior to the traditional image
recognition algorithm in terms of the accuracy of innovative application and development trend in the design,
and the processing time of influencing factors, which has obvious advantages. The innovative application and
development trend in design play an extremely important role in textile design, which can accurately predict
and optimize the growth characteristics and product generation of textile design. However, traditional image
recognition algorithms have certain limitations in solving simulation problems in innovative applications,
especially when dealing with complex problems. In this paper, this paper proposes innovative applications
and development trends in the design of algorithm platforms to better solve this problem. The scheme
accurately locates the influencing factors by comprehensively using the theory, so as to determine the division
of indicators, and uses the algorithm platform to construct the scheme. Experimental results show that under
certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different
problems, and it has better performance. Therefore, the use of simulation scheme based on algorithm platform
in the innovative application and development trend of textile design can better solve the limitations of
traditional image recognition algorithms and improve the accuracy and efficiency of simulation.
1 INTRODUCTION
The importance of innovative applications and
development trends in design in textile design is self-
evident (Wang, and Zhang, 2023). Through
simulation, various parameters and changes in this
process can be predicted and understood, providing
(Li, 2023) guidance and support for actual
production. However, there is certain deficiencies in
the accuracy of the innovative application (Chen,
Huang, et al. 2023) and development trend scheme in
the traditional design, which limits its effect in
practical (Zhang, 2023) application. In order to solve
the problem of accuracy of innovative applications
(Li et al., 2023) and development trends in traditional
design, researchers have introduced algorithm
platforms into the analysis of innovative applications
(Wu, 2023) and development trends in design in
recent years. The algorithm platform is a
computational (Shao, 2023) method based on group
behavior, which simulates the interaction and
cooperation between individuals (Yao, 2023) to
achieve the goal of global optimization. The
algorithm has the characteristics of decentralization
(Liu, 2023), immutability and smart contract, which
can effectively solve the accuracy problems existing
in traditional schemes. The optimization model
(Wang, and Jiang, 2023) of innovative application
and development trend in the design based on the
algorithm platform further improves the accuracy and
reliability of the simulation (Hang, 2023) by
optimizing the parameters and algorithms in the
process of innovative application (Yang, 2023) and
development trend in the design. The model adjusts
and optimizes the various parameters in this process
(Zhang, 2023) to achieve the best innovative
application results. At the same time, the model is
able to cope with complex environments and
66
Sun, Y.
Innovative Application and Development Trend of Algorithm Platform in Textile Design.
DOI: 10.5220/0013535600004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 66-73
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

interference factors, providing more realistic and
reliable simulation results. Through a large number of
experiments and data analysis, the researchers
evaluated the effectiveness of the optimization model
for innovative applications and development trends in
the design of algorithm platforms. The results show
that compared with the innovative application and
development trend scheme in traditional design, the
model has significant advantages in many aspects.
2 RELATED CONCEPTS
2.1 Algorithm Platform Processing
Method
Each core of the algorithm platform is set for different
needs, so as to improve the computational
performance or real-time performance of the
application. The intrinsic relationship between the
variables is constructed into a "model for dealing with
the innovative application and development trend of
sharing-creativity-design". The algorithm platform
has obvious advantages, which can carry out efficient
integration of archives and has the advantage of
sustainability. The algorithm platform analyzes the
innovative application and development trend in the
design of unstructured data, but it must meet the
following assumptions.
Hypothesis A: Pit is the development result of
innovative applications and development trends in
design, and the time is at time t, and the set of
innovative applications and development trends in
design (Det) is constructed. Among them, any data x
belongs to Pi, and the performance result of
innovative application and development trend in the
design is𝑃

(𝑥) as shown in equation (1).
𝑃

(𝑥)𝕊
𝛿𝑦
𝛿𝑥
= 𝜆𝛤
𝛥𝑦
𝛥𝑥
𝑥



,
(1
)
where belongs is𝑘∈(1, ⋯+ ∞) to the mapping
result. In order to improve the calculation accuracy of
innovative applications isℜ(𝑘) and development
trends in the design, the immunodeficient is𝐶(𝑥, 𝛽) of
x-sum is𝜁 integrated. In equation (1), if the
calculation accuracy is𝑥

= 𝜃(𝜌𝑡𝑎𝑛𝑡) low, and if it
is𝑥

<
∑
𝑥



,,
, the calculation accuracy meets
the requirements.
2.2 Classification of Innovative
Applications and Development
Trends in Design
Hypothesis B: The results of the analysis is𝑃

(𝑥) of
innovative applications and development trends in h'
s design, and the results of mobile technology in
innovative applications and development trends in
different designs, is𝜑(𝑥⋅𝑘) shown in equation (2):
𝑃

(𝑥)=

𝑘

∏







⎯
⎯

𝜀
∑
𝑓
(𝑃

(𝑥))


Π
(2
)
Among them, the comprehensive analysis
function of different dimensions.
Assuming C: The comprehensive classification
function i𝑓(𝑥) s a function that satisfies is𝑤(𝑥) the
following conditions is𝑤(𝑥)<℘, and , then the
judgment of the results of distributed computing
autonomous information technology is𝑤(𝑥)
″
<
℘


shown in equation (3).
𝑤(𝑥)
=

𝑘

𝑤(𝑥)

2
−𝑏±
√
𝑏

−4𝑎𝑐
2
𝑎
𝜙ℂ
(3
)
Hypothesis D: The innovative application and
development trend point in the arbitrary design is on
the axis of autonomous information technology
development, and the derivative of the innovative
application and development trend point in the
arbitrary design will represent the development
direction of information technology, which is𝑦

calculated as shown in equation (4).
𝐷(𝑦,
𝑓
(𝑦)

|
𝑝)=ΚΖ
±
√




(4
)
Among them, it is𝛼represents the development
direction of distributed computing independent
information technology.
From the above theorem, it can be seen that the
nonlinear relationship between different economic
data x can be calculated by using the innovative
applications and development trends in the design,
and the influence of i-dimension and t-time on the
results of economic characteristics can be reduced.
Therefore, the processing of innovative applications
and development trends in design provides a good
foundation and reduces the influence of data
structures on the results of innovative applications
and development trends in design. From theorem 3, it
can be seen that the multi-dimensional judgment
accuracy of independent information technology is𝛼⋅
Innovative Application and Development Trend of Algorithm Platform in Textile Design
67

𝑙𝑖𝑛(


) as follows, indicating that the multi-
dimensional judgment accuracy meets the
requirements, and further reduces the influence of
innovative applications and development trends in the
design on the results.
2.3 Innovative Application and
Development Trend Mining in
Design
In this paper, the algorithm platform is selected for
model construction, which is an innovative
application and development trend classification
technology in design, which has the advantages of
fuzziness and adaptability, and can realize cyclic
calculation and continuously revise the classification
set. The algorithm platform can be constrained by
using the IF mode to form constraint M, and its
classification process is as follows:
IF：𝑥

< 𝑑

，and 𝑀(𝑥) ∧𝐶(𝑥

, 𝑥

)，
(5
)
then 𝑦∧
∑
𝑦



∨


∑
𝑀(𝑥

)


∏
𝜀ℂ
(6
)
Among them, it is𝜆(𝑥

) the adjustment function of
autonomous information technology, the set of
adjustment results of distributed is computing
information technology, the constraints is𝑑

, and the
results of distributed computing autonomous
information technology. The algorithm platform
prepossess the distributed computing information
technology, and the algorithm platform is𝑀(𝑥) used
in the processing process, and the accurate results is𝑦
finally obtained. Therefore, the output results can be
deduced by the algorithm platform, and the results of
innovative applications and development trends in
comprehensive design can be obtained.
Hypothesis E: The innovative application and
development trend in any design is𝑥

that the
relationship between the input variable and the output
variable is𝑥

analyzed by constraint M, is 𝑦

shown in
equation (7).
𝛼⋅𝑔

𝔐∉= 𝑥⋅{
(𝑥

∧𝑐

)
𝑏

}

,


(7
)
Among them, the results of innovative
applications and development trends in different
designs;is𝑐

a prepossessing collection for
performance.
According to the calculation of the innovative
application and development trend in the above
design, the continuous operator of the innovative
application and development trend in the design is𝑏

obtained, and the calculation result is𝑔

shown in
equation (8).
𝑔

𝑛!
𝑟!
(
𝑛−𝑟
)
!
= ℤ𝜓  𝑔


∧(𝑥

)

,

,
(8
)
Among them, it is𝛿 the performance coefficient of
innovative application and development trend in
design, and k is the class. According to the results of
the innovative application and development trend in
the design, the output value of the innovative
application and development trend in the design can
be obtained, as shown in equation (9).
𝑦

𝑎

+ 𝑏

= 𝑔


(𝑥) ⇒
𝑓
(𝑃

(𝑥)

,

,
(9
)
The algorithm platform can shorten the
processing time of innovative applications and
development trends in the design, and increase the
amount of prepossessing performance. According to
the initial performance amount, the innovative
application and development trend in multi-
dimensional design is carried out (Zhang Tiantian
2023), and the results of innovative application and
development trend in continuous design is formed.
3 JUDGMENT OF INNOVATIVE
APPLICATION AND
DEVELOPMENT TREND
MODEL IN DESIGN
3.1 Initialization of Innovative
Applications and Development
Trends in Design
The innovative application and development trend
model in the design can improve the independent
information technology capability of distributed
computing and shorten the data structure optimization
time. It is reflected in the comprehensive analysis of
the initial data volume and multi-dimensional data
volume, and uses the innovative application and
development trend alarm conditions in the design to
realize the comprehensive judgment of the innovative
application and development trend in the design, and
output the optimal results.
(1) Innovative applications and development
trends in the independent design of distributed
computing
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68

In order to improve the computational accuracy of
the data, the researchers changed the structure of
distributed computing autonomous information
technology data by expanding the amount of initial
data and increasing the variety of data. The original
data structure was unstructured and discretely
distributed, but now it is more diverse. Under the
influence of expanding the amount of data and
increasing the variety of data, the amount of
performance data no longer conforms to the normal
distribution, but the problem of calculation
redundancy is solved, and the accuracy of
comprehensive calculation is improved. The specific
results can be referred to Figure 1.
Figure 1: Innovative applications and development trends
in the design of different processing aspects.
According to the comparison of the results shown
in Figure 1, it can be observed that the image
recognition algorithm and the algorithm platform
process the initial big data. Under the processing of
image recognition algorithms, the initial data volume
is relatively messy and non-directional. The amount
of data processed by the algorithm platform is
concentrated and directional. Based on theorems 1
and 2 of the algorithmic platform, the researchers
concluded that the results of the algorithmic platform
is independent of the spatial dimension, and that the
algorithm can more accurately handle the tasks of
innovative applications and development trends in the
design. In addition, when the algorithm platform
processes the initial amount of data, it can maintain a
consistent data distribution effect every time a point
is fetched, and the stability of the data set is high.
Therefore, in order to handle the initial amount of
data, it is a reasonable choice to choose an algorithm
platform.
(2) Comprehensive judgment strategy of
distributed computing information technology. In
order to realize the distributed collaboration and
comprehensive judgment of multi-dimensional
information technology, the algorithm adopts a
heterogeneous strategy and adjusts the corresponding
parameters to process performance data of different
dimensions. In the model, the big data is divided into
five multidimensional sub spaces, each of which
represents a dimension of the solution space. In the
iterative process, the innovative applications in these
five multi-dimensional designs evolve at the same
time as the development trend information. After the
iterative calculation is completed, the adaptation
values of each dimension is compared, and the
relationship between the position of the information
of the innovative application and the development
trend in each sub-multi-dimensional design and the
innovative application and the development trend
results in the comprehensive design is recorded.
Then, the most concise way is used to gradually learn
the innovative application and development trend
information in each sub-multi-dimensional design
and approach the optimal position of the
comprehensive design results, so as to improve the
speed and accuracy of the calculation of innovative
application and development trend in design. In this
way, the algorithm can use multi-dimensional
information synergy to complete the comprehensive
judgment process more effectively.
3.2 Innovative Application and
Development Trend Judgment
Technology in the Design of
Algorithm Platform
The basic idea of the algorithm platform is to make a
comprehensive judgment of innovative application
and development trend information in multi-time and
multi-dimensional design, and adjust and optimize
the innovative application and development trend
standards in the initial design of big data, and the
alarm conditions of innovative application and
development trend in the design to obtain the optimal
solution and reduce the independent information
technology rate of distributed computing, as shown in
Fig. 2.
Algorithm
Soft goods
Terrace
Devise
Innovate
Development
Appliance
Figure 2: Algorithm Platform and Big Data Calculation
Flow Diagram.
Innovative Application and Development Trend of Algorithm Platform in Textile Design
69

Here is the steps on innovative applications and
trends in distributed computing design:
1 Determine the design information and data
quantity structure: According to the data
characteristics and the need to solve the
problem, determine the data structure required
for innovative applications and development
trends in the design. The initial weight of the
whole data and the innovative application and
development trend alarm conditions in the
design is taken as a whole and mapped to the
big data, and the innovative application and
development trend information in the design
of each big data is taken as the product of the
weight and the alarm condition. According to
the actual application, the innovative
application and development trend
information in the big data design to
determine the development trend of this
development trend is D = 433.
2 Data initialization: Unstructured initialization
of relevant parameters of big data.
3 Generate fitness function: Generate the initial
amount of data using the algorithm platform
theory and map it to big data. The accuracy of
each big data is calculated and the absolute
value of its sum of squiss is used as a function
of fitness.
4 Determine the optimal position: Determine
the optimal location of innovative
applications and development trends in big
data design and the optimal position of each
sub field. The initial big data was divided into
five sub-data quantities, the fitness ratio was
calculated, and the comprehensive position
and the optimal position of each sub-data
volume were recorded.
5 Iterate the optimal position and velocity:
Among the 5 evolution of the amount of seed
data, choose one to evolve, and alliterative
update the optimal position and velocity
according to equations (7)~(9).
6 Iterative process: If the number of iterations is
less than the maximum number of iterations,
repeat steps 2~6, otherwise the iteration will
be stopped, and the results of the innovative
application and development trend alarm
conditions, weights, and best positions in the
design will be returned.
4 PRACTICAL CASE ANALYSIS
4.1 Performance Judgment of the
Model
The algorithm platform was tested with single-index
performance, multi-indicator performance, multi-
dimensional indicators and other indicators to verify
the performance of the model proposed in this paper.
Single-metric performance is the only minimum
function of the test model synthesis, and the formula
is as follows:
𝐴
(𝑥)=[𝑥


]



∂

Ω
∂𝑣

𝛾
(10
)
Multi-index performance is a cosine modulation
transfer function that frequently generates a single
minimum value to verify the practicability of the
model solution, and the formula is as follows:
𝐵(𝑥)=


𝑙𝑖𝑚
→∞
∑
𝑐𝑜𝑠𝛼𝑒







ℂ
(11
)
Multi-dimensional indicator is an algorithm used
to evaluate multi-dimensional data, and the judgment
speed of synthesis is calculated by gradient
optimization of multi-dimensional data. The specific
formula is as follows:
Indicator = Σ (weight i * value i)
where the weight i represents the weight of the ith
dimension, and the value i represents the numerical
value of the ith dimension. By multiplying and adding
the weights of all dimensions and the corresponding
values, the final metric value is obtained. This metric
value can be used to evaluate the performance of the
data across multiple dimensions, and the weights can
be adjusted to adjust the contribution of each
dimension to the final result.
𝐶(𝑥)=Τϒ −𝛼𝑒



∑




(12
)
where n is the total number of indicators for which
data is𝑥

calculated, and the number of arbitrary
indicators is used.
The calculation was simplified and a test
experiment with 1200 innovative application systems
was designed. In this experiment, we performed 30
iterations and set the maximum length to 24 months.
We tested each of the above three functions and
averaged the results 10 times. The specific calculation
results is shown in Table 1.
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Table 1: Detection results of different test functions
Test
metrics
Test the
function
Equatio
n
paramet
e
r
Standa
rd
Error
Wald
chi-
squid
95%
Confidence
interval
Single-
metric
performan
ce
Algorithm
ic
platform
0.3488 2.3331 1.4710 2.6079~0.1
640
Image
recognitio
n
algorithms
1.5890 0.2832 1.9927
Multi-
metric
performan
ce
Algorithm
ic
platform
0.3686 0.1457 3.0717 0.5460~1.2
811
Image
recognitio
n
algorithms
1.4262 2.1513 1.9777
Multi-
dimension
al metrics
Algorithm
ic
platform
1.1866 2.6480 0.9582 3.5760~0.2
947
Image
recognitio
n
algorithms
1.4513 3.7818 0.7362
The convergence plots for each data in Table 1 is
shown in Figure 3.
Figure 3: Comparative study of the research scheme of the
algorithm.
According to the data comparison in Table 1, the
algorithm platform proposed in this paper is closer to
the results of innovative applications and
development trends in comprehensive design,
compared with image recognition algorithms. In
terms of standard deviation, mean, value range, etc.,
the algorithm platform performs better. As can be
seen from the surface changes in Figure 3, the
algorithm platform performs better in terms of
stability and judgment speed. Therefore, the
algorithm platform is better in terms of judgment
speed, performance level judgment and summation
stability.
4.2 Innovative Applications and
Development Trends in Design
The judgment data set of innovative applications and
development trends in design includes innovative
applications and development trends in digital design,
innovative applications and development trends in
bionic design, innovative applications and
development trends in natural design, innovative
applications and development trends in psychological
design, and innovative applications and development
trends in expected design [22]. After the preliminary
prepossessing of the data, 43 rows of structured data
and 32 rows of semi-structured data were obtained. In
order to facilitate information efficiency, data in
different fields is selected, namely: financial field,
public service field, information security field, and
Internet of Things field, and the data processing
results is shown in Table 2.
Table 2: Classification and proportion of innovative
applications and development trends in design.
Different types Mean SD
Wearable technology 43.60 0.98
Smart textiles 44.12 0.90
3D printing technology 45.34 0.85
Smart manufacturing 43.79 1.24
Test Items Test value p-value
-2Ln LR(L^2) 15.52 0.34
Pearson chi-squid 12.61 0.55
Scaled Deviance 15.52 0.34
Degrees of freedom= 14
4.3 Test Results
In order to verify the algorithm platform proposed in
this paper, the results is compared with image
recognition algorithms and big data, and the results is
shown in Figure 4.
Figure 4: Test results for different algorithms.
According to the data in Figure 4, the algorithm
platform surpasses image recognition algorithms and
big data in terms of accuracy, and the error rate is
Innovative Application and Development Trend of Algorithm Platform in Textile Design
71

relatively low. This shows that the calculation results
of the algorithm platform and big data is relatively
stable, and the difference between the calculation
results of the algorithm platform and big data is small.
Table 3 shows the average results of the above two
algorithms.
Table 3: Comparison of judgment accuracy at different
levels
algorithm Size of
sampl
es
Mean
R
Se 99%
Confidence
interval
P-
value
Accura
cy
Algorith
mic
platform
624 0.612
8
0.724
0
0.6622~0.7
694
0.721
2
1.4413
Image
recogniti
on
algorithm
s
626 0.736
5
3.72
38
0.6390~0.8
160
0.134
2
4.1364
According to the data in Table 3, there is problems of
insufficient accuracy and large variation of
calculation results in the innovative application and
development trend judgment of image recognition
algorithms and algorithm platforms in different levels
of design. compared with the algorithm platform, the
algorithm constructed in this paper has a significant
improvement in accuracy. At the same time, the
accuracy of the algorithm and the algorithm platform
constructed in this paper is similar, both higher than
80%, which is better than the image recognition
algorithm. In order to further verify the superiority of
the algorithm platform, I also compared the optimal
fitness values of different algorithms, and the results
is shown in Figure 5.
Figure 5: Performance process of eigenvalues.
According to the results of Figure 5, it is obvious
that the algorithm platform performs better in image
recognition. The reason for this result is that the
algorithm platform continuously improves
performance by increasing strategies of different
dimensions such as synergy coefficients, improved
weights, and convergence factors.
5 CONCLUSIONS
Aiming at the accuracy of innovative applications and
development trends in design, a new comprehensive
optimization scheme is proposed, which is based on
algorithm platform and advanced computer
technology. Initially, the security of information and
the credibility of tampering with it were ensured by
using the decentralized nature of the algorithm
platform and its data consistency guarantee. Then,
combined with computer technology, the collected
data is deeply analyzed and processed in detail, so as
to dig out the intrinsic attributes and potential value
of the data. The study also delves into the key
performance indicators needed to ensure that
innovative applications and trends in design is
accurate and credible, and constructs a
comprehensive web-based information collection
platform that plays a critical role in ensuring the
accuracy of research outputs. However, it is worth
noting that when applying the algorithm platform, the
selection of the evaluation system for innovative
applications and development trends in the design
must be cautious, so as to effectively explore and
utilize the advantages of the algorithm platform and
further improve the accuracy and practical
application value of the research results.
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Shao Ziwei (2023) The Innovation of Bada Halo Pattern in
Textile Pattern Design Western leather, 45 (18), 102-
104
Yao Wei (2023) The application of diversified materials in
textile design Western leather, 45 (20), 21-26
Liu Wei (2023) The application of modern decorative
patterns in household textile design Footwear Craft and
Design, 3 (18), 62-64
Wang Dongchen,&Jiang Pei (2023) The application of
digital printing patterns in textile art design Journal of
Pu'er University, 39 (4), 104-106
Wang Yang,& Zhang Fan (2023) The Application and
Innovative Design of New Materials in the Field of
Textile Design - Review of the Application of Textiles
in Interior Design Leather Science and Engineering, 33
(6), I0006
Yang Zhonghua (2023) Innovation and Practice of Textile
Design Technology - Review of Practical Textile
Design Technology Woolen Technology, 51 (8), I0009
Zhang Zhaobo (2023) Research on the application of
nanomaterials in textiles Engineering and Management
Science, 5 (3), 43-45
Zhang Tiantian (2023) The application of ethnic element
fabric textiles in indoor soft decoration design Dyeing
and finishing technology, 45 (8), 78-80
Innovative Application and Development Trend of Algorithm Platform in Textile Design
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