An automatic planning method for a simulated knee surgery based on deep learning, comprising: acquiring a medical image of a knee joint, and converting a pixel pitch in the medical image into an actual length; selecting a key regional image in the medical image, and performing object detection on the key regional image to extract key points based on deep learning; and calculating a corrective angle with coordinates of the extracted key points by labeling the key points and a mechanical axis, calculating a rotated angle, simulating bone cutting and bone rotation to obtain a surgery planning image, and performing automatic planning on a high tibial osteotomy (HTO) and a distal femoral osteotomy (DFO), characterized in that, the converting the pixel pitch in the medical image into the actual length comprises: acquiring a ruler image from the medical image by, screening out white pixels in the medical image, and detecting the ruler image with a line segment detector (LSD); and acquiring a head endpoint coordinate and a tail endpoint coordinate of a ruler in the ruler image, and performing conversion on a pixel difference value between the head endpoint coordinate and the tail endpoint coordinate (X A and X B ) of the ruler in an X direction and a millimeter unit to obtain a coefficient of relationship α between the millimeter unit and the pixel difference value, α = X A − X B / 18 10 . acquiring a medical image of a knee joint, and converting a pixel pitch in the medical image into an actual length; selecting a key regional image in the medical image, and performing object detection on the key regional image to extract key points based on deep learning; and calculating a corrective angle with coordinates of the extracted key points by labeling the key points and a mechanical axis, calculating a rotated angle, simulating bone cutting and bone rotation to obtain a surgery planning image, and performing automatic planning on a high tibial osteotomy (HTO) and a distal femoral osteotomy (DFO), characterized in that, the converting the pixel pitch in the medical image into the actual length comprises: acquiring a ruler image from the medical image by, screening out white pixels in the medical image, and detecting the ruler image with a line segment detector (LSD); and acquiring a head endpoint coordinate and a tail endpoint coordinate of a ruler in the ruler image, and performing conversion on a pixel difference value between the head endpoint coordinate and the tail endpoint coordinate (X A and X B ) of the ruler in an X direction and a millimeter unit to obtain a coefficient of relationship α between the millimeter unit and the pixel difference value, α = X A − X B / 18 10 . acquiring a ruler image from the medical image by, screening out white pixels in the medical image, and detecting the ruler image with a line segment detector (LSD); and acquiring a head endpoint coordinate and a tail endpoint coordinate of a ruler in the ruler image, and performing conversion on a pixel difference value between the head endpoint coordinate and the tail endpoint coordinate (X A and X B ) of the ruler in an X direction and a millimeter unit to obtain a coefficient of relationship α between the millimeter unit and the pixel difference value, α = X A − X B / 18 10 . <EOS>