Aiming at the problem that the overload of information resources of TV products leads to the difficulty of user selection, this study mainly focuses on the improvement and application of article-based collaborative filtering algorithm in television product recommendation system, and combines the personalized recommendation technology with TV product system to meet the need of users and operations. In the recommendation process, the user's preference data model is first collected, and the duration of the user watching the television product is taken as the explicit characteristics of the user's preference. Then, it is improved by introducing the weight of on-demand amount in the traditional collaborative filtering algorithm, and the Euclidean distance method is used to calculate the similarity of the items. Finally, the viewing time of the target user on the television products is predicted according to the neighbor set, and a recommendation result is obtained. Experiments show that the introduction of on-demand amount weights can improve the accuracy of recommendations.