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In recommender systems, models mostly use a combination of embedding layers and multilayer feedforward neural networks. The high-dimensional sparse original features are downscaled in the embedding layer and then fed into the fully connected network to obtain prediction results. However, the above methods have a rather obvious problem, that is, the features directly input are treated as independent individuals, and in fact there are internal correlations between features and features, and even different features have different importance in the recommendation. In this regard, this paper adopts a self-attentive mechanism to mine the internal correlations between features as well as their relative importance. In recent years, as a special form of attention mechanism, self-attention mechanism is favored by many researchers. The self-attentive mechanism captures the internal correlation of data or features by learning itself, thus reducing the dependence on external sources. Therefore, this paper adopts a multi-headed self-attentive mechanism to mine the internal correlations between features and thus learn the internal representation of features. At the same time, considering the rich information often hidden between features, the new feature representation obtained by crossover between the two is likely to imply the new description of the user likes the item. However, not all crossover features are meaningful, i.e., there is a problem of limited expression of feature combinations. Therefore, this paper adopts an attention-based approach to learn the external cross-representation of features.