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In the era of big data, data information has exploded, and all walks of life are impacted by big data. The arrival of big data provides the possibility for the realization of intelligent financial analysis of enterprises. At present, most enterprises’ financial analysis and decision-making based on the analysis results are mainly based on human resources, with poor automation and obvious problems in efficiency and error. In order to help the senior management of enterprises to conduct scientific and effective management, the study uses big data web crawler technology and ETL technology to process data and build an intelligent financial decision support system integrating big data together with Internet plus platform. J Group in S Province is taken as an example to study the effect before and after the application of intelligent financial decision support system. The results show that the crawler technology can monitor the basic data and the big data in the industry in real time, and improve the accuracy of decision-making. Through the intelligent financial decision support system which integrates big data, the core indexes such as profit, net asset return, and accounts receivable of the enterprises can be clearly displayed. The system can query the causes of financial changes hidden behind the financial data. Through the intelligent financial decision support system, it is found that the asset liability ratio, current assets growth rate, operating income growth rate, and financial expenses of J Group are 55.27, 10.38, 20.28%, and 1,974 million RMB, respectively. The growth rate of real sales income of J Group is 0.63%, which is 31.27% less than the excellent value of the industry 31.90%. After adopting the intelligent financial decision support system, the monthly financial statements of the enterprises increase significantly, and the monthly report analysis time decreases. The maximum number of financial statements received by the Group per month is 332, and the processing time at this time is only 2 h. According to the results, it can be seen that the intelligent financial decision support system integrating big data as the research result can effectively improve the financial management level of enterprises, improve the usefulness of financial decision-making, and make practical contributions to the field of corporate financial decision-making.

With the need of social and economic development, the audit method is also continuously reformed and improved. Traditional audit methods have defects of comprehensively considering various risk factors, and cannot meet the needs of enterprise financial work. To improve the effectiveness of audit work and meet the financial needs of enterprises, a solution for intelligent auditing of enterprise finance is proposed, including intelligent analysis of accounting vouchers and of audit reports. Then, Bi-directional Long Short-Term Memory (BiLSTM) neural network is used to classify the audit problems under three text feature extraction methods. The test results show that the accuracy, recall rate, and F1 value of the COWORDS-IOM algorithm in the aggregate clustering of accounting vouchers are 85.12, 83.28, and 84.85%, respectively, which are better than the self-organizing map algorithm before the improvement. The accuracy rate, recall rate, and F1 value of Word2vec TF-IDF LDA-BiLSTM model for intelligent analysis of audit reports are 87.43, 87.88, and 87.66%, respectively. This shows that the proposed method has good performance in accounting voucher clustering and intelligent analysis of audit reports, which can provide guidance for the development of enterprise financial intelligence software to a certain extent.

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]