In order to solve the problem of long time and high cost of traditional detection methods for falling block in railway tunnel lining. Based on the acoustic signal recognition technology, this paper proposes a sound detection method for lining falling block in railway tunnels based on GA-SVM. After extracting the MFCC characteristic coefficient matrix of the lining falling block and other event sounds in the railway tunnel, this method uses the optimization ability of genetic algorithm to optimize the two parameters C and σ that affect the accuracy of the prediction model in support vector machine, so as to construct the railway tunnel lining dropped block detection model. The test results show that compared with the traditional SVM model and the PSO-SVM model, the GA-SVM model can more accurately detect the size of fallen lining blocks on the basis of a small number of training samples, and the detection accuracy reaches 96. 67%, which verifies the feasibility of the application of acoustic signal recognition technology in the detection of lining falling block of railway tunnels