The invention provides a facial reconstruction method based on a non-supervision automatic encoder. The main content of the facial reconstruction method based on a non-supervision automatic encoder includes a semantic code vector, a decoder based on a parameter model and a loss layer. The process of the facial reconstruction method based on a non-supervision automatic encoder includes the steps: scene description is given in a semantic code vector mode; the parameter decoder generates a composite image corresponding the face, and a reverse image is formed through standard reverse propagation, and then end-to-end training without supervision is realized, and the parameter decoder includes an image forming model, a lighting model, image formation and reverse propagation; and a loss function is defined by three items, and the loss layer includes dense luminosity calibration, sparse landmark alignment, statistical regularization and reverse propagation. The facial reconstruction method based on a non-supervision automatic encoder can encode the details of the face, such as posture, shape, expression, skin color and scene lighting, is more exquisite, does not need supervision and allows end-to-end learning. Compared with a network of synthesizing face data training, the network can be preferably popularized to real data.