Study on the Method and System of Rural Tourism Recommendation based on Seasonal Characteristics and Location Characteristics Under AHP Algorithm

This article proposes a rural tourism recommendation method and system research based on AHP algorithm, which addresses the problem of insufficient analysis of location and seasonal features in current rural tourism recommendation systems. This method first uses the AHP algorithm to perform weight analysis on user needs and tourist attraction features. Then, seasonal and location features are considered as supplementary factors, and combined with tourist attraction interest and distance factors, the final recommendation result is calculated. At the same time, in order to achieve rapid data processing and calculation, this article also designed a web-based interactive system interface for the rural tourism recommendation system. To verify the effectiveness of this method and system, we conducted experiments and evaluations on real rural tourism datasets. The experimental results show that this method and system can improve the accuracy and efficiency of recommendation. Compared with methods based on traditional recommendation algorithms, the methods and systems proposed in this article can better address the preferences of users in different scenarios of rural tourism recommendation, while also considering the influence of factors such as season and location, providing users with more personalized and accurate recommendation services. In summary, the rural tourism recommendation method and system design based on seasonal and location features proposed in this article can make up for the shortcomings of current rural tourism recommendation systems in terms of location and seasonal feature analysis, and can effectively improve recommendation accuracy and efficiency. This method and system have broad application prospects in the field of rural tourism recommendation and tourism planning.