The invention discloses a traffic jam judgment method based on deep learning. The traffic jam judgment method comprises the following steps: 1, acquiring a training sample, and adding a tag so as to obtain an image which comprises the tag and corresponds to a monitoring video file; 2, performing forward propagation, namely, transmitting the image which comprises the tag and corresponds to the monitoring video file into a designed convolution neural network model, and performing forward propagation so as to obtain a type tag output by the convolution neural network model; 3, performing back propagation, namely, calculating a loss function value of the type tag output in forward propagation and an actual type tag of the sample, performing back propagation on the loss function value in a minimized error direction so as to adjust a weight matrix of a convolution layer and obtain a final convolution neural network model; 4, judging traffic jam, namely, transmitting at least one frame of image corresponding to a current monitoring video file of a selected road section into the trained final convolution neural network model, and performing forward propagation. By adopting the traffic jam judgment method, the traffic jam grade can be provided according to the traffic situation of a current road, and relatively good applicability and robustness can be achieved.