Over the last few years, microblogging is increasingly becoming an important platform for users to acquire information and publish some reviews or personal status. In microblogging, hash tags mean some topic words between two '#', such as some social events or some hot topics. Hash tags can highlight the topic of tweets, and make tweets be easily searched and understood by others. Therefore, many users like adding hash tags for their tweets. Existing hash tag recommendation methods always consider semantic similarity between hash tags and tweets as the only key factor. However, the hash tags' user acceptance degree and development tendency are two important factors for evaluate the recommendation probability of them. In this paper, we propose a model for recommending some related hash tags for users to choose one or more of them as content added into forthcoming tweets. The above three factors have been considered to complete this task, which are the semantic similarity between a hash tag and a tweet, the user acceptance degree of the hash tag, and the development tendency of the hash tag. Experimental results on a dataset from Sina Weibo, one of the largest Chinese microblogging websites, show usefulness of the proposed model for recommending hash tags to forthcoming tweets.