本文在结构化地方财政收入的基础上,依次使用灰色关联度和灰色预测模型找出影响地方财政收入构成的关键因素,进而利用与其对应的关键因素建立BP神经网络模型,将各因素的灰色预测值代入模型中求得地方财政收入的构成值,汇总后得到地方财政收入.最后根据实证结果进行数据分析,并结合历史数据对结果进行修正,从而得出较优的预测模型.%Based on the structured local fiscal revenue, this paper uses grey relational degree and grey prediction model in turn to find out the key factors influencing the local fiscal revenue composition. Then the corresponding key factors are used to establish the BP neural network model, the grey predictive values of the factors are substituted into the model to get the composite values of local fiscal revenue, the values are gathered to get the local fiscal revenue. Finally, according to the empirical results, the data analysis is carries out, combined with the historical data, the results are modified to get the optimal prediction model.
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