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Under the influence of the COVID-19, great changes have taken place in China's economic structure and market subjects and college students are facing difficulties in employment. By carrying out collaborative innovation between schools and enterprises, cultivating high-quality talents to meet social needs has become an important way to solve difficulties. Based on the input-process-output principle, a performance evaluation index system for collaborative innovation is constructed, which includes 3 primary indicators and 22 secondary indicators. The weights of each indicator are determined using improved AHP. A mathematical model for evaluating the performance of school enterprise collaborative innovation is established using the fuzzy comprehensive evaluation method. The evaluation value of the innovation and entrepreneurship education case project is 0.82, indicating that the overall operation is in good condition. The cooperation between schools and enterprises has created a favorable atmosphere and accumulated rich experience for carrying out innovation and entrepreneurship education. Good results have been achieved in talent cultivation and scientific and technological output, but there are also problems such as low conversion rates of scientific and technological achievements. Based on the evaluation results, the article proposes suggestions to improve the performance of collaborative innovation between schools and enterprises. The research results provide theoretical reference for the performance evaluation of school enterprise collaborative innovation and the practice of innovation and entrepreneurship education. [ABSTRACT FROM AUTHOR]

As a key component of enterprise assets, accounts receivable play an important role in enterprise financial management and determine the long-term development of enterprises in the later period. In order to minimize the financial risk brought by the credit sales of enterprises, this subject studies the intelligent optimization of enterprise financial account receivable management. BP neural network and K -means clustering algorithm are used to evaluate the risk of account receivable and the owner's credit, respectively. The account balance accounts for 45.20% of the total amount, and the risk rating of accounts receivable is 4. The training result of BP neural network algorithm has high accuracy. With K -means clustering algorithm, accurate evaluation of owner's credit can be achieved, which can provide reference for optimization of enterprise account receivable management mode. [ABSTRACT FROM AUTHOR]