Research on the optimization of consumer preference and tourism destination marketing combination strategy

As the competition in the tourism market intensifies, the precision marketing strategy is crucial. This study focuses on the machine learning-based optimization model of tourist destination marketing. Integrate multi-source data in data processing, and use cleaning and integration technology to ensure the quality. The model algorithm selects multiple and exquisite. In supervised learning, the decision tree is accurately divided by the information gain of tourist characteristic neural network MLP digs deep the nonlinear associations by back propagation; in unsupervised learning, K-Means and Gaussian mixed model effectively cluster and subdivide the tourist group. All algorithms often work together to provide a comprehensive insight into consumer preferences, accurately create products for tourism destinations to improve marketing efficiency, and promote the development of the tourism industry.