The exponential growth of the internet has led to the creation of a large volume of information, making it challenging for users to navigate through. As a solution to this challenge, recommendation systems have emerged. In video recommendation, besides applying some basic interaction data (including image data, behavior data, context data, etc.) to the recommendation model, many studies also try to apply the video content data to the model for video recommendation. In this paper, we propose different types of features for different modal data, and select the most suitable feature types according to different tasks. The internal representation of features is learned by a multi-headed self-attentive mechanism and the cross-representation of features is learned by attention. On the basis of this, the multimodal features of video data are represented and modeled in a unified way, which is the basis for the implementation of multi-view video recommendation. and based on this, we add multimodal video content to mine richer and more comprehensive descriptions, so as to provide more accurate and personalized recommendations. The results of experiments conducted on real data sets demonstrate the effectiveness of the model proposed in this paper.