In recent years, as deep learning has demonstrated powerful characterization capabilities in the fields of speech, image, and text, researchers have begun to apply it to the field of prediction, i.e., predicting customer behaviors through current interaction records and features. This paper proposes a deep wandering-based customer behavior prediction model that combines deep learning techniques to forecast customer behavioral trends in the cultural industry. The model randomly wanders from the social network graph structure of the customer’s purchase of goods to generate a new behavioral sequence. We regard the user’s behavioral sequence as a word, and we pre-train all the behavioral sequence documents using the Word2vec algorithm model. The experimental comparison revealed that the model, which incorporates the depth-wandering technique, outperforms other models on the test set in terms of predictiveness. The website uses the deep wandering user behavior prediction model to forecast sales and adapts its sales strategy based on the customer’s behavior. 31% of customers were content with the books they bought from the website, while 52% were extremely content. By comparing the book sales before and after applying the model, it was found that the book sales increased significantly after adjusting the sales strategy, indicating that the customer behavior prediction model constructed in this paper can be used practically.