Most personalized recommendation based on user and products, without combined with environmental factors and physical features, so that the algorithm can’t be a good predictor of consumer preferences. This paper presents a mixed strategy of personalized recommendation algorithm FACCF a CTAP (Customer-Time-Area Package) probabilistic topic model, where the customer behavior fusion domain features, spatial characteristics, pay tendencies and product features; The algorithm is based on the time-domain, area- domain collaborative filtering characteristics of the spatial behavior; after filtration new features, product themes and product features to optimize the combination; later, based on Bayesian network customer groups pay a tendency to cluster analysis, the formation of secondary optimization, get a personalized recommendation list. Use real customer behavior data to conduct empirical analysis results show that the FACCF algorithm show more accurately predict the customer"s consumption, and enhance personalized recommendation accuracy.