Abstract: The harmonious and stable development of society is strongly related to the sustainable development of enterprises. In order to better face the challenges of environmental resources, sustainable development must be included in the development focus of accounting enterprises. The research proposes a performance evaluation model based on deep learning, improves RBMs model on the basis of deep belief network (DBN), improves the accuracy of the model through reverse fine-tuning technology, and effectively combines multiple restricted Boltzmann machines (RBMs) and Softmax classifiers to build a modular multi classification model to complete the sustainable development performance evaluation of accounting enterprises. The performance of RBM fine tuning classifier is higher than that of RBM expression and PCA (Principal Component Analysis) expression, which mainly shows the effectiveness and stability of feature extraction. The network output results of test samples are converted into prediction performance evaluation. The model is evaluated by average precision (AP), average recall (AR), and prediction accuracy. The AP, AR, and prediction accuracy of the proposed method are 86.95%, 89.74%, and 88.29% respectively, which are higher than Softmax classifiers, Back Propagation (BP) neural networks, and DBN based Softmax methods, It shows that this method is superior to other algorithms in the application of performance evaluation model for sustainable development of accounting enterprises, and it is feasible and effective, which is of great significance to the establishment of performance evaluation model for the accounting industry.

Cui Hu, “A Deep Learning-based Model for Evaluating the Sustainability Performance of Accounting Firms” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131273

@article{Hu2022,
title = {A Deep Learning-based Model for Evaluating the Sustainability Performance of Accounting Firms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131273},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131273},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Cui Hu}
}