Integration and Application of an Intelligent Content Classification Model Based on Artificial Intelligence Technology and Metadata in Web Applications

With the explosive growth of Internet information, Web applications are facing the challenge of efficient classification and management of a massive amount of content. Traditional classification methods rely on manual rules, which are inefficient and difficult to adapt to dynamically changing content. This study proposes an intelligent content classification model based on artificial intelligence technology and metadata, and integrates it into web applications to achieve automated and precise content classification and management. Preprocessing operations such as cleaning, deduplication, and word segmentation on multimodal data such as text, images, and videos in web applications, and extract key metadata information such as title, author, publication time, tags, etc., are performed. Pre-trained language models and image feature extraction models are used to extract high-dimensional feature representations of text and images, respectively, and metadata information are combined to construct a comprehensive feature vector. Deep neural networks are used to learn from annotated training data and construct a classification model. The experimental results illustrate that compared with traditional methods, the proposed model has significantly improved in accuracy, recall, and F1 score, reaching 95.2%, 94.8%, and 95.0%, respectively. The proposed intelligent content classification model based on artificial intelligence technology and metadata can effectively solve the problem of content classification in web applications, and improve content management efficiency and user experience.