A logistics composite code identification method based on multitask depth learning comprises a method for acquiring logistics composite code images in all directions, a label design scheme convenientfor visually detecting and positioning a spray code character, a design scheme suitable for the character positioning of the label of the spray code character, a segmented character size, a font and an interval among the characters, a composite code design scheme suitable for visual identification, a Faster R-CNN network used for detecting and positioning a composite code, an algorithm module usedfor composite code image rectification and the forward / backward detection of the characters, a multi-task deep convolutional neural network used for deep learning and training identification, a convolutional neural network based on the character identification on the label of the spray code character of deep learning, the algorithm module used for identifying a one-dimensional barcode in the composite code, the algorithm module used for identifying the two-dimensional barcode in the composite code, and a sorting control module used for controlling sorting action according to identified composite code information. In the invention, a problem that a lot of randomly-placed, irregular-shaped, flexible packaging cargos can not be quickly and automatically sorted is effectively solved.