关键词: 关键词: 智能生成文本, 特征融合, 生成文本检测, 文本分类, 人工智能

Abstract: Abstract: To address the problems of highly realistic AI-generated text caused by the rapid development of large language models and the performance degradation of traditional detection methods, an intelligent text detection approach based on multi-feature adaptive fusion was proposed. Firstly, a language style feature set covering text statistical features, language structural features, and language uncertainty features was constructed to capture the differences between real texts and generated texts; meanwhile, deep semantic features of texts were extracted using independent encoding technology. Based on these, a dual-path mapping feature adaptive fusion strategy was designed: language style features and deep semantic features were first fused at a primary level, then further integrated through deep learning to enhance the adaptability and representational power of the fused features. Experimental results demonstrate that the proposed method achieves detection accuracies of 98.1% on the Chinese experimental dataset and 98.5% on the English TuringBench dataset. Compared with the best-performing baseline method J-Guard, improvements of 2.1 and 2.4 percentage points are observed on the Chinese and English datasets, verifying the effectiveness of the proposed approach. Focused on the issue that the rapid development of Large Language Model results in highly realistic AI-generated text and performance degradation of traditional detection methods, a detection method for AI-generated texts based on multi-feature adaptive fusion is proposed. This method first constructs a linguistic style feature set integrating textual statistical features, linguistic structural features, and linguistic uncertainty features to capture the discrepancies between authentic and AI-generated texts. Simultaneously, an independent encoding technique is employed to extract deep semantic features of the text. Subsequently, a dual-path mapping feature adaptive fusion strategy is designed: initially fusing the linguistic style features with deep semantic features, followed by secondary fusion based on deep learning mechanisms to enhance adaptive feature fusion capability. Experimental results demonstrate that the proposed method achieves detection accuracies of 98.1% and 98.5% on a self-constructed Chinese dataset and the TuringBench English dataset, respectively, representing average improvements of 6.67% and 7.29% compared with baseline models.

Key words: Keywords: AI-generated text, feature fusion, generated text detection, text classification, artificial intelligence