The invention discloses a semantic image segmentation method and system based on deep learning and clustering, and the method comprises the following steps: S1, carrying out the convolution and pooling of an original image through a convolution neural network, and obtaining a linear feature matrix of the original image; S2, performing subspace clustering on the linear feature matrix to obtain clustered feature data; and S3, performing deconvolution and up-sampling on the clustered feature data, and processing the clustered feature data to pixels the same as those of the original image to obtain a segmented image. According to the method, the convolutional neural network (CNN) in the deep neural network is combined with subspace clustering, and the sparse subspace is used for replacing a full connection layer in the CNN, so that the problems of complex semantic image segmentation calculation, large data volume and poor information in the prior art are solved. A subspace clustering method is introduced into the neural network, so that a large amount of marking data required by the CNN during working is reduced, and unsupervised learning of the CNN neural network is realized.