In order to respond to the regional coordinated development of the country, it is necessary to put forward a method that can predict and analyze the development trend according to the current development situation. In view of this, the research will carry on the present situation and forecast analysis to the coordinated development of urban agglomeration in Western China. Firstly, the 3E system is used to establish the regional coordination degree evaluation model, and on this basis, the ellipsoid model is introduced for better coordination degree evaluation. In addition, in order to improve the prediction ability of the model, the convolution neural network is used to realize the big data analysis of the model. The results show that the overall coordination degree of the western urban agglomeration is in a weak coordination state in 2015, but the coordination degree of the region will reach 147.35 in 2020. The results show that the overall coordination degree of western urban agglomeration will gradually show a good trend, but the change speed is slow. The above results show that the prediction model in the study has strong practicability, the calculation results can fit the current situation, and the good prediction ability can provide decision-making suggestions for many governments.

中文翻译：

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基于卷积神经网络算法的区域协调发展预测预警

为了应对国家区域协调发展，有必要提出一种能够根据当前发展形势预测和分析发展趋势的方法。鉴于此，本研究将对西部城市群协调发展进行现状及预测分析。首先利用3E系统建立区域协调度评价模型，在此基础上引入椭球模型以更好地评价协调度。此外，为了提高模型的预测能力，利用卷积神经网络实现模型的大数据分析。结果表明,2015年西部城市群整体协调度处于弱协调状态, 但2020年该地区协调度将达到147.35。结果表明，西部城市群整体协调度将逐步呈现良好趋势，但变化速度较慢。上述结果表明，本研究的预测模型具有较强的实用性，计算结果能够贴合当前情况，良好的预测能力可以为多国政府提供决策建议。