Users' demand for personalized travel services is becoming stronger and stronger. Providing more intelligent travel suggestions to users has become a hot topic in academia and industry. This paper mainly studies the alication of deep learning in urban tourism route decision suort. Due to the advantages of CNN network in text feature extraction, the design uses CNN network to process user comment information and tourism service project comment information respectively. The network is designed with four layers: input layer, convolution layer, pooling layer, and fully connected layer. A set of tourism service recommendation model based on deep learning is constructed and implemented. Through the experimental comparison with the common traditional recommendation methods, it can be found from the experimental results that the proposed model method is significantly better than the traditional recommendation methods, which verifies the superiority of the model.

Zhonghui Chen. Deep Learning in Urban Tourism Route Decision Suort. International Journal of Neural Network (2021), Vol. 2, Issue 3: 1-8. https://doi.org/10.38007/NN.2021.020301.

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