To address the problems posed by highly realistic AI-generated text， driven by the rapid development of Large Language Models （LLMs）， and the performance degradation of traditional detection methods， an adaptive multi-feature fusion detection method for AI-generated text was proposed. Firstly， a language style feature set covering text statistical features， language structural features， and language uncertainty features was constructed to capture differences between real and AI-generated texts； then， 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， and secondary fusion was then performed using deep learning to enhance the capability of adaptive feature fusion. Experimental results demonstrate that the proposed method achieves detection accuracies of 98.1% on the Chinese SocialAI-Detect dataset and 98.5% on the English TuringBench dataset； compared with the best-performing baseline， J-Guard （Journalism Guided adversarially robust detection of AI-generated news）， the improvements are 2.3 and 2.1 percentage points， respectively， verifying the effectiveness of the proposed method.