Multi-round Conversational Recommendation System （CRS） obtains real-time information of users interactively， thus performing better than traditional recommendation methods such as collaborative filtering based method. However， existing CRS suffers from problems inaccurate mining of user preferences， too many conversational rounds required and inappropriate recommendation moments. Aiming at these problems， a new conversational recommendation algorithm based on deep reinforcement learning considering user’s multi-granularity feedback information was proposed. Different from existing CRS， in each conversation， the feedback of users on items themselves and more fine-grained item attributes was considered by the proposed algorithm at the same time. Then， users， items and attribute features of items were updated online by using the collected multi-granularity feedback， and the environment state after each round of conversation was analyzed by Deep Q-Network （DQN） algorithm. As a result， more appropriate and reasonable decisions were made by the system， and the reasons of why user buying items were analyzed and the users’ real-time preferences were mined comprehensively with fewer conversation rounds. Experimental results on two real datasets show that compared with Simple Conversational Path Reasoning （SCPR） algorithm， the proposed algorithm has the 15 turns success rate increased by 46.5%， and the 15 average turns decreased by 0.314 rounds in Last.fm dataset， while it maintains the same level of success rate but the 15 average turns decreased by 0.51 rounds in Yelp dataset.