The invention discloses a semi-supervised image semantic segmentation method and device based on a self-supervised low-rank network, and the method comprises the steps that: the self-supervised low-rank network is constructed, the inverse geometric transformations are performed masks from two branches, a pseudo mask is generated through an optimization module, and the pseudo mask is input into an LR low-rank module; in each iteration, an assignment matrix P is calculated through softmax normalization attention and a temperature coefficient; the optimal basis mu is updated by aggregating the input feature X, and after a softmax normalized class activation graph A with the class being C and a deep feature X1 are obtained, the kth initialization basis is calculated through a weighted average value; and, in the base initialization process, a target function composed of classification loss and pseudo mask segmentation loss is used for supervision, an output result of an LR low-rank module is decoded and optimized, and the self-supervision low-rank network is updated according to the loss. The device comprises a construction module, an optimization module, an LR low-rank module, an updating module, a prediction module, a supervision module and an output module.