The end-customer recommendation system is an effective tool for large-scale manufacturer terminal marketing. How to design a search method for finding the best target customer by collecting global market data in the Internet+ environment has become a challenge. To solve this problem, This study proposes a terminal customer recommendation method based on global market data perception (GMF). That is to use the idea of global analysis to preprocess the customer data nationwide, establish a comprehensive, multi-angle evaluation index, and obtain the target customer value. Then, through the method of domain subspace decomposition, the data is decomposed and analyzed in the domain subspace, and the customer evaluation criteria in a certain region are obtained. The analysis results of the two are effectively merged, and the similarity of the coupled objects is calculated, and the most similar TopN data is used as the best target customer result set. The experimental results on the data set generated by the large-scale manufacturer marketing activities show that the proposed algorithm is significantly better than the current mainstream collaborative filtering algorithm.