The invention discloses a personalized user tag modeling and recommendation method based on a unified probability model, comprising the following steps: S1, carrying out statistics on tagging behaviors of users on a social tagging site; S2, carrying out formal definition on questions tagged by the users; S3, establishing a topic model based on user tagging, wherein the topic model is a unified probabilistic model and called a UdT model; S4, establishing a frame of a tag recommendation system based on the UdT model, wherein the frame is recommended through learning the interest of the users and according to semantic information included in the interest; and S5, verifying the frame of the tag recommendation system. Experimental results show that by using the method of the invention, user interest can be effectively explored and the accuracy of tag recommendation can be improved.