As a common implementation of recommender system，collaborative filtering can bring personalized recommendation service experience to users． Traditional collaborative filtering models do not mine and analyze the attention level of different explicit attributes of users and items，leading to the critical level of different explicit attributes not paid attention by the model． Therefore，based on the coupled collaborative filtering model based on convolutional neural network，an attention mechanism was introduced in the paper to deeply mine the critical degree of explicit attributes and enhance the parameter learning gradient on critical attributes． And a new method of calculating the coupling degree was proposed to ensure the flushness of parameters and to improve the recommendation performance of the model． The experimental results show that the recommendation accuracy rate of the model proposed in the paper is better than that of traditional collaborative filtering methods and coupled collaborative filtering models，and the cumulative gain of topK@ 10 hit ratio and normalized discount cumulative gain reach 0. 8508 and 0. 5850，respectively．