The interactive development mode of combining sports industry with tourism industry has attracted more and more people’s attention and gradually been accepted by people. The organic combination of the two can effectively promote the healthy growth of regional economy. The development and operation mode of minority STRs (sports tourism resources) has become an important topic in the research of sports tourism development. It is not only important for the successful integration of minority traditional sports into tourism industry but also of great significance for promoting the development of sports tourism industry. In this paper, the personalized sports tourism service recommendation system based on multi-objective optimization is studied, and a tourism service combination method based on multi-objective optimization is proposed. This method is based on multi-objective optimization, and the historical data of tourists and their current preferences are considered, respectively. The converted data are used to train the RF (random forest) model offline, and online recommendation only needs to be scored and predicted according to the rules of the RF model. The results show that the online recommendation time of the proposed algorithm is basically below 100 s, which is much lower than that of other recommendation algorithms. The experimental results show that setting the weights of user evaluation information and related tourism information can further improve the matching degree between recommendation results and users’ needs.

中文翻译：

---

基于随机森林算法的民族体育旅游资源开发策略研究

体育产业与旅游产业相结合的互动发展模式越来越受到人们的关注，并逐渐为人们所接受。两者有机结合，可有效促进区域经济健康发展。少数民族STRs（体育旅游资源）开发运营模式已成为体育旅游开发研究的重要课题。这不仅对少数民族传统体育成功融入旅游产业具有重要意义，而且对于促进体育旅游产业的发展也具有重要意义。本文研究了基于多目标优化的个性化体育旅游服务推荐系统，提出了一种基于多目标优化的旅游服务组合方法。该方法基于多目标优化，分别考虑游客的历史数据和当前偏好。转换后的数据用于离线训练RF（随机森林）模型，在线推荐只需要根据RF模型的规则进行评分和预测即可。结果表明，该算法的在线推荐时间基本在100 s以下，远低于其他推荐算法。实验结果表明，设置用户评价信息和相关旅游信息的权重，可以进一步提高推荐结果与用户需求的匹配度。转换后的数据用于离线训练RF（随机森林）模型，在线推荐只需要根据RF模型的规则进行评分和预测即可。结果表明，该算法的在线推荐时间基本在100 s以下，远低于其他推荐算法。实验结果表明，设置用户评价信息和相关旅游信息的权重，可以进一步提高推荐结果与用户需求的匹配度。转换后的数据用于离线训练RF（随机森林）模型，在线推荐只需要根据RF模型的规则进行评分和预测即可。结果表明，该算法的在线推荐时间基本在100 s以下，远低于其他推荐算法。实验结果表明，设置用户评价信息和相关旅游信息的权重，可以进一步提高推荐结果与用户需求的匹配度。远低于其他推荐算法。实验结果表明，设置用户评价信息和相关旅游信息的权重，可以进一步提高推荐结果与用户需求的匹配度。远低于其他推荐算法。实验结果表明，设置用户评价信息和相关旅游信息的权重，可以进一步提高推荐结果与用户需求的匹配度。