With the in-depth application of natural language processing technology in complex tasks such as multilingual understanding and knowledge reasoning, the heterogeneity, noise interference and insufficient cross-modal adaptation of large-scale corpora have become the key bottlenecks restricting the improvement of the semantic cognitive ability of models. In response to this challenge, this study proposes an innovative technical framework that integrates dynamic data governance and multimodal collaborative modeling. First, a dynamic corpus cleaning mechanism based on active learning is designed. The text quality is quantified by constructing a two-way semantic entropy difference index, and the real-time iterative optimization of the corpus is achieved by combining an incremental learning strategy. Secondly, a cross-modal aligned GNN. Graph Neural Network) architecture is proposed, which uses a multi-level attention mechanism to map text, image, and speech data to a unified semantic space, and improves the fusion efficiency of multimodal features through knowledge distillation technology. Finally, a distributed corpus sharing platform based on differential privacy is developed to achieve collaborative mining of cross-domain resources while ensuring data security. Experimental results show that in the multilingual disambiguation task, the average F1 value of the proposed method is 90.1%, which is 18.8% higher than that of the traditional method. In the cross-modal information retrieval task, the Top-1 accuracy of image and text retrieval and speech retrieval increased by 16.1% and 14.8%, respectively. In the dynamic corpus cleaning efficiency test, the processing efficiency of the proposed method increased by more than 5 times, and the noise removal rate increased to 89.3%. These results verify the effectiveness of the proposed method in improving the processing efficiency of large-scale corpora and the accuracy of multimodal tasks.