Friend recommendation is a fundamental service for both so-cial networks and practical applications.The majority of existing friend-recommendation methods utilize user profiles,social relationships,or static post content data,but rarely consider the semantic intentions and dynamic behaviors of users.In this paper,we propose FRec++,a friend recommendation method based on semantic and dynamic information.In FRec++,the first plus stands for new friend recommendation using a semantic model based on LDA while the next plus stands for the use of dynamic user attributes(e.g.,behaviors or positions).More specifi-cally,we first use the LDA method to generate semantic topics for user interests,and then compute the topic similarities between the target us-er and candidate friends.Next,we calculate the similarities of dynamic behaviors(i.e.,forwarding,making comments,liking,and replying)and investigate the static attributes of users to measure the relevance of pref-erences.Finally,the weighted average method is used to integrate the above factors.We conducted experiments on the Weibo dataset and the results show that FRec++outperforms several existing methods.