Planar array capacitive imaging is an emerging detection technique that has the advantages of being non-invasive, having a fast response time, and the ability to approach an object being measured from one side. However, the presence of soft field effects leads to the non-uniform distribution of sensitivity and the existence of negative sensitivity regions caused by the presence of the guard electrodes, which affects the quality of image reconstruction. To address this problem, an iterative average pooling method for sensitivity distribution optimization is proposed. A three-dimensional model of the sensor is established, the initial sensitivity coefficient matrix is calculated using the finite element method and the distribution characteristics of the sensitivity matrix are analysed. Inspired by neural networks, the convolution operation is introduced into the original sensitivity matrix. Then the 3 × 3 convolution operation is utilized to extract sensitivity data features. Iterative average pooling is proposed to reduce the uneven distribution caused by the soft field effect. The strategy of using the average pooled sensitivity values as parameters in the next calculation is proposed to prevent sudden changes in the sensitivity matrix and ensure a consistent trend with the original matrix. The experimental results show that the proposed method can attenuate the influence of soft field effects and effectively improve the homogeneity of the sensitivity distribution and quality of image reconstruction.