To accurately recommend power sales packages to users and help them select the package that best meets their needs, a deep interest evolution network（DIEN）algorithm based on deep learning is proposed. First, a comparison of several recommendation models is conducted to assess the performance of DIEN. Subsequently, an analysis of the structure of the interest evolution layer and the model’s hyperparameters is performed. Then, aiming at the“long tail effect”observed in the application of DIEN model in electricity market domain,two gating mechanisms are introduced between the interest extraction layer of the original model and the user vector. Finally, the feasibility of the proposed method is verified through a case analysis. Results show that proposed method can improve the electricity package adaptation rate of electricity users, enhance the market competitiveness of the power company and bidirectional benefits for both of the power company and users.