In the current era of information explosion, existing recommendation algorithms often ignore data privacy protection, leading to potential security risks for user information. This paper aims to study the Web front-end intelligent recommendation and encryption algorithm based on big data technology. First, data collection is carried out, and user behavior data is obtained from multiple e-commerce platforms through crawler technology; secondly, user behavior is analyzed, and clustering algorithms are used to segment users and extract key features of users; next, a recommendation model is built, combining collaborative filtering and deep learning algorithms, and the model is trained using the TensorFlow framework; finally, the AES encryption algorithm is used to encrypt user data to ensure data security. The recommendation system based on this method has achieved a recommendation accuracy of 98.4%. The method of combining intelligent recommendation with encryption algorithm proposed in this paper not only improves the performance of the recommendation system, but also provides effective protection for user data security.