Web Interactive Content Filtering Model Integrating Prompt Classifier and Intent Understanding
DOI:
https://doi.org/10.13052/jwe1540-9589.2578Keywords:
Content filtering, prompt classifier, intent understanding, web interactive content, large language modelAbstract
To accurately identify and intercept harmful content generated in user interactions on Web platforms such as social media and online forums, this study proposes a Web interactive content filtering model that integrates Prompt classifiers and intent understanding. The model adopts a hierarchical review architecture with dual-module collaboration. The front-end deploys a Prompt classifier based on Deep Contrastive Learning (DCL), and uses the attention mechanism and deep convolutional network to perform rapid semantic analysis and preliminary screening of real-time comments and conversations. The back-end introduces an intent understanding module based on search-enhanced generation and Large Language Models (LLMs) to conduct in-depth contextual analysis of preliminary screening warning content to distinguish malicious attacks from legitimate sensitive topic discussions. Experiments showed that the system performed well on a test set containing 52,397 Chinese Web interactive texts. The Prompt classifier module achieved a precision of 95.2% and a recall of 96.5%. After collaborative calibration with the intent understanding module, the false alarm rate of the system was significantly reduced to 4.4%, while the recall rate remained at 96.1%. From the results, this study provides a content security solution for Web interactive scenarios that combines high accuracy, strong semantic understanding capabilities, and real-time response performance. It can effectively purify the cyberspace environment while ensuring freedom of interaction, with important practical application value.
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