Abstract Traditional personalized news recommendation methods still face several limitations, such as inadequate modeling of dynamic user interests, difficulty in balancing accuracy and diversity, and significant performance degradation in cold-start scenarios. These limitations hinder their effectiveness in real-world applications. To address these issues, a Personalized News Recommendation Model via a Fully Connected Neural Network (MT-FCNN) is proposed.