The objective is to enable international trade companies to understand their situation in a timely and rapid manner and make optimal sustainable development decisions. The improved balanced scorecard model is applied to the construction of the evaluation index system for the sustainable development of international trade. Through the literature survey, the causal relationship between various indexes is analyzed. Eventually, taking the performance evaluation model with 5 first-level indexes, 10 second-level indexes, and 25 third-level indexes as the finance dimension, client dimension, internal process dimension, learning and growth dimension, as well as environment dimension is formed. The COFCO International Trade Platform is selected as a case, and 300 questionnaires are issued to evaluate its performance. Then, BP (Back Propagation) neural network is used as the model. AHP (Analytic Hierarchy Process) method is combined to determine the weight of each index, further obtaining the actual evaluation model. 15 companies are selected to test the model. The results show that the application of the balanced scorecard model is conducive to improving the company's understanding of the performance evaluation management of international trade platforms. The neural network model can also accurately evaluate the performance of international trade companies, and it is highly practical, which can provide a healthy and sustainable development idea for the performance evaluation of international trade companies.

Yuxuan Zhao. Influence of Deep Learning Neural Network on Performance Evaluation of Sustainable Development of International Trade. Academic Journal of Business & Management (2020) Vol. 2, Issue 2: 32-43. https://doi.org/10.25236/AJBM.2020.020205.

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