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This paper proposes a point-of-interest (POI) sequence recommendation algorithm based on BERT-ACNN-GRU to address the issues faced by the existing POI recommendation model in social network large data, such as the difficulty in extracting deep feature information and the low recommendation performance. Firstly, the semantic relationship between a word and its context in the text is combined using the bidirectional encoder representation from transformers (BERT) model to effectively eliminate the influence of word distance and obtain the contextualized word vector. Secondly, a convolutional neural network (CNN) utilizing a gated recurrent unit (GRU) is employed to capture the feature information of the text. Lastly, the attention method is utilized to assign weight scores to various terms in order to provide more attention to particular words and boost the precision of recommendations. The experiments demonstrate that the precision, recall rate, F1 score and mean average precision (mAP) of the proposed method are 0.097, 0.26, 0.103, and 0.085 on the Gowalla dataset when the recommendation list has a length of 10, respectively. On the Yelp dataset, the precision is 0.093, the recall rate is 0.26, F1 is 0.099, and MAP is 0.089. Hence, the proposed method can effectively enhance the performance of the POI recommendation system.

With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists’ preferences and tourist attractions, and incorporate the attention mechanism to enhance the model’s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists’ transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists’ travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists’ interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model’s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model’s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development.