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Copyright of Chinese Journal of Dermatovenereology is the property of Xi'an Jiaotong University Periodicals Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Dermatovenereology is the property of Xi'an Jiaotong University Periodicals Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Dermatovenereology is the property of Xi'an Jiaotong University Periodicals Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Existing text classification models insufficiently utilize global and local information in texts, leading to subpar classification performance, In response to this issue, a text classification model called global and local features dynamic fusion (GLFDF) is proposed. The GLFDF model was initially designed with a dynamic fusion enhancement module to dynamically control the inte- gration of global temporal features and local semantic features into specific positions of the word embedding matrix. Subsequently, the embedding matrix where global and local features fused was fed into a feature extraction module for further processing. Finally, the proposed model was tested on two public datasets, Ohsumed and THUCNews. Experimental results show that the GLFDF model achieves F1 scores of 63,24% and 92.50% on these datasets, respectively, surpassing other advanced text classification models and enhancing text classification performance. From the analy- sis of the ablation experiment, the dynamic fusion enhancement module fully makes the global temporal features and local semantic features of the text fused together, effectively solving the problem of insufficient use of global and local information in the text classification model. [ABSTRACT FROM AUTHOR]