Text Mining-based Enterprise Financial Performance Evaluation in the Context of Enterprise Digital Transformation

Abstract

As enterprises gradually move towards digitalization, it is increasingly difficult to accurately evaluate changes in corporate financial performance. To improve this situation, the study uses a text mining algorithm based on the web crawler principle to extract keywords from corporate annual reports, select representative financial performance indicators through IF-FDP, and construct a corporate financial performance evaluation model using the entropy weighting method. The performance comparison experiments of the text mining algorithm proposed in the study show that the accuracy-recall rate area under the line of the text mining algorithm proposed in the study is 0.83 and the average F-value is 0.34, which are both better than other algorithms. In the empirical analysis of the financial performance evaluation model, it was found that the financial performance evaluation model had the smallest absolute error of 0.3%, which was lower than the other models. The above results indicate that both the text mining algorithm and the performance evaluation model proposed in the study outperform the comparison algorithm and model. Therefore, the performance evaluation model proposed by the study can be used to effectively evaluate the financial performance of enterprises accurately and promote the development of enterprises, which has practical application value.