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Research on data-driven optimization of cross-border e-commerce copywriting and artwork

Shaolin Hu

Shaolin Hu

Department of International Trade, Anhui Institute of International BusinessHefei, China
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Hu, Shaolin
  
29 sept 2025
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Publicado en línea: 29 sept 2025
Recibido: 14 ene 2025
Aceptado: 10 may 2025
DOI: https://doi.org/10.2478/amns-2025-1133
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© 2025 Shaolin Hu, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Structure diagram of topic control text generation model based on cross-term
Structure diagram of topic control text generation model based on cross-term

Figure 2.

Structure of cross-term coding model based on keyword topic control
Structure of cross-term coding model based on keyword topic control

Figure 3.

SF-GAN-V2 network architecture
SF-GAN-V2 network architecture

Figure 4.

Automatic evaluation results
Automatic evaluation results

Figure 5.

Manual evaluation results
Manual evaluation results

Figure 6.

Loss value decline during training
Loss value decline during training

Figure 7.

Comparison of accuracy after 100 rounds of training
Comparison of accuracy after 100 rounds of training

Figure 8.

Topic similarity
Topic similarity

Figure 9.

The objective index evaluation results of the model in the DS1
The objective index evaluation results of the model in the DS1

Figure 10.

The objective index evaluation results of the model in the DS2
The objective index evaluation results of the model in the DS2

Hyperparameter details

Hyperparameter Data set
Learning Rate 0.00001
Warmup Steps 380
Eval Period 105
Beam Size 4
Length Penalty 1.3
Optimizer Adam
Num Nodes 48
Num Relations 57
Embedding 769
va 0.17
Wa 0.48
Batch Size 45
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Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
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