At present, economic business activities occur to the accounting element confirmation link of the accounting information system (AIS), which still occupies a large amount of manpower and material resources of the enterprise to generate information. This has been restricting the development of modern AIS. Based on this, first, this paper studies the influence of the knowledge economy on accounting innovation and in the innovation economy. The process of confirmation for accounting elements in economic business, under the traditional AIS, is analyzed. Then, the model of the accounting element confirmation, i.e., BPNN, is constructed by combining the backpropagation (BP) neural network (NN) theory with the artificial intelligence (AI) algorithm. Finally, we simulate the confirmation process of accounting elements based on the economic business data of specific online stores. The experimental outcomes illustrate that under the proposed BPNN model, the output value of the accounting entry is also increasing and has been in the interval [0, 0.14] with the continuous increase in the input value of economic business activities. Moreover, the overall simulation error of economic business activity data does not exceed 0.3% in the simulation test of the proposed accounting business data confirmation model based on the BPNN algorithm. The empirical outcomes indicate that the model has high accuracy and reliability. The purpose is to realize the identification of business events by machines and complete the automatic confirmation of accounting elements in the back-end economic business of online stores. This paper provides new ideas for realizing the overall intelligence of the AIS.

中文翻译：

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创新经济背景下人工智能算法在会计领域的应用分析

目前，经济业务活动发生在会计信息系统（AIS）的会计要素确认环节，仍然占用企业大量的人力物力来生成信息。这一直制约着现代AIS的发展。基于此，本文首先研究了知识经济对会计创新和创新经济的影响。分析了传统AIS下经济业务中会计要素的确认过程。然后，将反向传播（BP）神经网络（NN）理论与人工智能（AI）算法相结合，构建了会计要素确认模型BPNN。最后，我们根据特定网店的经济业务数据模拟会计要素的确认过程。实验结果表明，在提出的BPNN模型下，随着经济业务活动输入值的不断增加，会计分录的输出值也在增加，并且一直处于区间[0, 0.14]内。此外，在提出的基于BPNN算法的会计业务数据确认模型的仿真测试中，经济业务活动数据的整体仿真误差不超过0.3%。实证结果表明该模型具有较高的准确性和可靠性。目的是实现机器对业务事件的识别，完成网店后台经济业务中会计要素的自动确认。