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Construction of Educational Tourism Seeking Prediction Model in the Context of Smart Tourism

Yan Wang

Yan Wang

Ordos Institute of TechnologyOrdos, China
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Wang, Yan
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Lina Fu

Lina Fu

Ordos Institute of TechnologyOrdos, China
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Fu, Lina
  
Sep 29, 2025
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Published Online: Sep 29, 2025
Received: Jan 07, 2025
Accepted: Apr 24, 2025
DOI: https://doi.org/10.2478/amns-2025-1094
Keywords
© 2025 Yan Wang and Lina Fu, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Variational autoencoder model
Variational autoencoder model

Figure 2.

Schematic diagram of the LSTM network
Schematic diagram of the LSTM network

Figure 3.

The monthly number of visitors to the global market is reached
The monthly number of visitors to the global market is reached

Figure 4.

Part of the impact factors and the trend chart of visitors to the o region
Part of the impact factors and the trend chart of visitors to the o region

Figure 5.

Effectiveness validation of the SAE model
Effectiveness validation of the SAE model

Figure 6.

Prediction of various models
Prediction of various models

Figure 7.

Prediction of different models
Prediction of different models

Validity of the LSTM model

Data set Model MAE RMSE MAPE R2
City A SAE-BPNN 2406.7771 2955.4755 13.2938 0.9591
SAE-LSTM 1767.4436 2161.279 7.7297 0.9997
City B SAE-BPNN 642.8299 829.4109 14.6134 0.9596
SAE-LSTM 468.5335 594.6993 11.0552 0.9985

The influence of tourism in o region

Tourist category Influencing factor
Dining O province food, O province restaurant, O province food festival
Lodging O province hotel, O province accommodation
Transportation O province ferry, O province flights
Tour O province travel, O province map, O province travel agency, O province tourism
Clothing O province weather, O province weather forecast
Shopping O province shopping, O province shopping mall
Recreation O province bar, O province show, O province night life

Performance indicators for each model

Data set Model MAE RMSE MAPE R2
City A BPNN 2697.0785 3320.968 12.7449 0.8985
SAE-LSTM 1746.884 2152.5106 5.8985 0.9974
City B BPNN 749.9702 956.3858 19.8099 0.9399
SAE-LSTM 487.4811 567.7616 10.4415 0.9985

Effectiveness validation of the SAE model

Data set Model MAE RMSE MAPE R2
City A LSTM 2435.7387 3147.7083 11.5892 0.9033
SAE-LSTM 1750.559 2150.0587 4.6727 0.9798
City B LSTM 607.6683 785.9161 9.8639 0.9319
SAE-LSTM 474.0651 588.808 3.0481 0.9742
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