Due to the timeliness of activities in Event-Based Social Network （EBSN）， the traditional social network recommendation algorithms cannot be applied to EBSN. In addition， most of the traditional recommendation algorithms ignore the feedback that can affect whether the previous users accept the recommendation， which influences subsequent recommendation quality. Therefore， an activity recommendation method based on directed label graph and user feedback in EBSN was proposed. Firstly， EBSN was abstracted into a directed label graph， and a Directed Graph Structure Feature （DGSF） index was construction by extracting the property feature information of nodes and edges to filter nodes and edges for the first time. DGSF index consists of node property feature index， directed edge property feature index and time feature index. Secondly， a multi-attribute candidate set filtering strategy based on DGSF index was proposed. By using the limits of time， in-degrees and out-degrees of nodes， and label types， the further pruning of the candidate sets was realized to avoid redundant computation. Thirdly， an improved UCB （Upper Confidence Bound） activity recommendation algorithm with user feedback was put forward， namely EN_UCB （Elastic Net UCB）. In EN_UCB， with the introduction of the elastic net regression， the interest values of the user to the activities were calculated according to many influencing factors， and the activities with high interest values were recommended to the user. At the same time， the feedback whether the user accepted the activities was received to optimize the subsequent user recommendation. Experimental results show that EN_UCB has the accept rate higher than TS （Thompson Sampling）， UCB and eGreedy， the regret rate far lower than TS and eGreedy， the running time superior to TS， UCB and eGreedy， and the larger the number of activities， the more obvious the advantages. The proposed method implements online activity recommendation in EBSN effectively.