In this paper, the video retrieval technique based on model migration makes full use of the migration learning technique to build a text encoder and video encoder to obtain text and video features. The text is preprocessed based on the BERT model, and text features are extracted by combining TF-IDF and mutual information. The logistic regression algorithm is applied to achieve text classification. The text mining techniques above are used to parse YouTube Chinese learning resources to extract their dissemination features. The results show that 5,286 (61.48%) of YouTube Chinese learning resources are from the category of “education,” and 2,294 (26.68%) are from the category of “people and blogs.” The video resources category is the most popular and loved category among learners due to its highest average number of views (1238214.3). The dissemination is significantly impacted by teachers’ performance, vivid language, and interaction during the teaching process. The indirect effect value test between online interaction, technical support, and resource quality, as well as satisfaction and intention to continue using, met the criterion of significance (p<0.05). It indicates that perceived usefulness has a significant indirect effect (P<0.01) on the impact of knowledge dissemination. This study clarifies the dissemination influences that led to the rise of Chinese resources in the global context.