To explore the effect of applying multi-layer convolutional neural network to noise removal during the acquisition and transmission of functional motion images, the classical image denoising algorithms of mean filtering, median filtering, and wavelet transform filtering are first introduced. In addition, the evaluation methods of mean square error (MSE), image enhancement factor (IEF), peak signal to noise ratio (PSNR) and structural similarity measure (SSIM) in the image quality evaluation index system are introduced. Based on the convolutional neural network model, a multi-scale parallel convolutional neural network (MP-CNN) model is constructed to remove the noise in the image, and the functional action image is devoted to different degrees. Finally, the denoising effect is evaluated by the objective and subjective evaluation system. The objective evaluation results show that MP-CNN's MSE, IEF, PSNR, and SSIM are better than the single-channel model, and the test time is shorter. The subjective evaluation results show that the MP-CNN model has the best effect on noise removal after 25 denoising of functional action images. In this study, a multi-channel image denoising model based on the multi-layer convolutional neural network can improve the effect of functional motion image noise removal. KEYWORDSMulti-layer convolutional neural network, Functional action-image, Noise, Evaluation system METHODOLOGY Image denoising algorithmNoise generally has the characteristics of random distribution, and the probability density function can be used to describe the noise distribution, which can be divided into Gaussian, Rayleigh, exponent, γ, uniform and impulse noise and so on. The classical denoising methods include mean filtering, median filtering and wavelet transform filtering. Mean filtering is a common spatial linear filtering method, which plays an obvious role in smoothing noise, and the Box template is often used to filter noise images.