To overcome the shortcomings of data acquisition and analysis methods in the existing artificial anal sphincter system, a fecal perception reconstruction system is designed. The system mainly includes the design of a sensor module, a data acquisition and storage module, and a data analysis algorithm. Through the cooperation of the multi-sensor data acquisition and the storage module, the distribution of rectal surface pressure is reconstructed. A fecal perception prediction model based on principal components analysis (PCA) and support vector machines (SVM) is proposed (PCA-SVM). The results show that the axial and radial pressure signals of the upper arm and the radial position of the middle arm are significantly related to the fecal perception. The SVM algorithm based on the Gauss kernel function and the penalty factor C=0.0595 and the width σ=0.9536 pairs of effective pressure indicators are selected. Compared with the feed-forward neural network model, the vector model has a higher prediction accuracy and meets the requirements of the fecal perception function of the artificial anal sphincter system.