The impact of technology optimisation incorporating machine learning algorithms on the financial sustainability of new energy companies

Limin Zhang and Zhixue Li

International Journal of Knowledge-Based Development, 2023, vol. 13, issue 2/3/4, 181-197

Abstract: New energy companies in the industry have differences in financial performance from traditional companies. To help new energy companies develop sustainably, it is necessary to analyse and monitor their financial characteristics. To optimise the financial technology of new energy enterprises, this research uses GoogleNet convolutional neural network to construct a financial risk analysis model, which can judge financial risks and issue early warnings based on enterprise financial data. The experimental results of the financial risk analysis model show that the test loss value of the model is as low as 2.97%, which is very close to the loss value of the training set. The financial risk analysis model shows a large advantage over similar models, with an accuracy rate of 91.14%. In addition, the model's predictive ability and the actual situation are well fitted with an overall accuracy of 85%. In general, the outstanding performance of this model is that its judgment accuracy is significantly higher than that of similar models, and the timeliness of early warning is significantly higher than that of human early warning.

Keywords: convolutional neural network; machine learning; new energy; financial analysis; sustainable development. (search for similar items in EconPapers)
Date: 2023
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