In-depth quantitative studies regarding the nonlinear development laws of a region and the calculation of construction land area (CLA) and weight coefficients of different influencing factors are of great significance for establishing the developmental change model of a region. In this study, a novel calculation method using correlation coefficients was introduced. First, models of three neural network algorithms, namely, back propagation neural network (BPNN), grey model neural network (GMNN), and generalised regression neural network (GRNN), were constructed. Key attention was given to improvement of the BPNN. The correlation laws of different influencing factors in a region and the CLA were discovered and extracted using these three models. The coefficient of determination and coefficient of variation were applied to verify the validity of the simulation results for each neutral network algorithm model. The mean absolute error and root mean square error of the three algorithm models were calculated to select the neutral network algorithm model with the highest accuracy. Subsequently, the mean impact value algorithm was added to the selected algorithm model to calculate the weight coefficients for the different relevant factors. The calculated results were compared with the weight values of influencing factors, which were calculated using the analytic hierarchy process, thus forming a set of calculation methods for a more accurate judgement of the influencing factors and weight coefficients of CLA. In this study, the correlations between the influencing factors and CLA were calculated using the proposed method via a case study. The calculated results of the proposed method conformed well to the practical situation in Ya’an City, indicating that the proposed method is worthy of promotion and practice.

深入定量研究一个区域的非线性发展规律，计算建设用地面积（CLA）和不同影响因素的权重系数，对于建立一个区域的发展变化模型具有重要意义。在这项研究中，介绍了一种使用相关系数的新计算方法。首先，构建了三种神经网络算法的模型，即反向传播神经网络（BPNN）、灰色模型神经网络（GMNN）和广义回归神经网络（GRNN）。重点关注BPNN的改进。使用这三个模型发现和提取区域内不同影响因素与CLA的相关规律。应用决定系数和变异系数来验证每个中性网络算法模型的仿真结果的有效性。计算三种算法模型的平均绝对误差和均方根误差，选择精度最高的中性网络算法模型。随后，在选定的算法模型中加入平均影响值算法，计算不同相关因素的权重系数。将计算结果与采用层次分析法计算的影响因素权重值进行比较，从而形成一套计算方法，以更准确地判断CLA的影响因素和权重系数。在这项研究中，通过案例研究，使用所提出的方法计算了影响因素与CLA之间的相关性。所提方法的计算结果与雅安市实际情况吻合较好，值得推广和实践。