Multi label innovation and entrepreneurship data classification method based on data mining

To address the challenges of low efficiency and poor accuracy in classifying innovation and entrepreneurship data, this study proposes a multi-label classification method for innovation and entrepreneurship flow databased on data mining techniques. The methodology begins with data acquisition through web crawling technology, followed by comprehensive data cleaning. Subsequently, stream data features are extracted using TF-IDF in data mining techniques. A multi-label classification model is then constructed by integrating Chinese webpage classification information through the PageCNN architecture. The final stage involves consolidating classification results from multiple single-label classifiers to generate multi-label outputs. Experimental results demonstrate that the proposed method achieves a classification accuracy of 97.1% with a processing time of only 2.1 s, significantly improving both the efficiency and effectiveness of innovation and entrepreneurship flow data classification.