This paper proposes a nondestructive detection method for detecting wall disease by employing multi-spectral imaging based on convolutional neural networks. This method aims to address issues such as low detection efficiency and easy interference by subjective factors that are associated with the use of artificial survey methods in traditional wall disease detection. The minimum noise separation method is used to preprocess the multispectral imaging data of a city wall, which reduces the dimensions of the data while preserving the original data features and reducing data noise. To address the problem of low classification accuracy caused by mixed and diverse pixels of different types of wall damage, a convolution operation is used to extract the features of wall damage, with the most important features retained and irrelevant features removed, resulting in a sparse network model. The extracted features are integrated and sorted through a full connection layer. Two dropout are included to prevent overfitting. Finally, on a wall multispectral dataset, the trained convolution neural network classification model is used to detect wall damage at the pixel level, and the predicted results are displayed visually. Experimental results show that the overall accuracy and Kappa coefficient are 93.28% and 0.91, respectively, demonstrating the effectiveness of the proposed method, which is crucial for enhancing the detection accuracy of wall disease and fully understanding its distribution.