The invention discloses a method for learning driving styles based on a self-coded regularization network. The method mainly comprises the following steps: performing GPS (Global Positioning System) data conversion, performing regularization network self coding, performing target function sum approximation, establishing a run length encoding frame, and establishing the number of drivers, namely, in a group of unknown driving, inputting GPS data of vehicles establishing a statistic characteristic matrix as network input, introducing a marker of a limited training set as a prior into an unsupervised automatic encoder, reconstructing hidden layer RNN (Recurrent Neural Network) characteristics, extracting a neck layer of a regularization self-coding structure as a final driving style characteristic representation layer, and estimating the number of drivers in the driving process. By adopting the method, the limit that the driving style of an unknown driver is hard to describe can be solved, a self-coded regularization network is designed to directly learn driving habits of the driver from the GPS data, then recognition and classification precision of different drivers can be improved, and a relatively safe and accurate method can be provided for design of assistant and automatic driving systems.