Short video content and recommendation algorithm based on deep learning

Existing short video content analysis methods often struggle with integrating multimodal data, resulting in incomplete content understanding and limited recommendation accuracy. To address these challenges, this study proposes a deep learning-based short video content analysis and recommendation algorithm that enhances feature extraction and personalization. CNN and LSTM are employed to automatically capture and extract multimodal features from short videos, generating a comprehensive feature representation through weighted fusion. Meanwhile, DNN is utilized to model user behavior, extracting deep behavioral features to enhance preference prediction. By jointly modeling video content and user behavior, a multimodal recommendation algorithm is developed to optimize content delivery. Experimental results show that, compared to CF, MF, and Content-Based Filtering benchmark algorithms, the proposed method improves average precision by 4.7%, 3.3%, and 4.0%, and recall by 3.5%, 1.2%, and 2.1%, respectively. These findings confirm that the deep learning-driven approach effectively enhances multimodal content understanding, meets personalized user preferences, and significantly improves recommendation accuracy, offering a more intelligent and adaptive short video recommendation framework.