The recommendation performance can be effectively improved by combining explicit and implicit feedback, but the existing recommendation methods fail to retain the information reflecting the degree of user preference in explicit feedback. Moreover, existing studies consider that the data with explicit feedback has the same impact on the model as the data with only implicit feedback and fail to give full play to the advantages of explicit feedback. To solve these problems, this paper proposes a new collaborative filtering recommendation model combining explicit and implicit feedback(CEICF). Firstly, the feature of explicit feedback is extracted to get the global preference vector of user/item, and the latent vector of user/item is extracted from implicit feedback. Then, these two vectors are integrated to get the preference vector of user/item. Finally, a neural network is used to predict the possibility of user's interaction with items. And when training the model, a weighted binary cross-entropy loss function is defined, which strengthens the influence of explicit feedback on the model to enhance the model's ability to capture user preferences. In order to verify the effectiveness of the proposed model, this paper conducts experiments on real datasets covering different domains. The results show that the CEICF can effectively integrate explicit and implicit feedback, and the recommendation effect is significantly improved compared with the baseline models.