This application relates to the artificial intelligence field, and discloses a data processing method. The method includes: processing target data through a target neural network to obtain a data processing result, where a target header of the target neural network is used to process, through a first transformation matrix, a first vector corresponding to first subdata, and process, through a second transformation matrix, a second vector corresponding to the first subdata, where the first vector corresponds to position information of the first subdata in the target data, and the second vector corresponds to semantic information of the first subdata. In this application, a matrix size of a transformation matrix corresponding to a position vector is set to be less than a size of a matrix corresponding to a semantic vector. To be specific, a size of the first transformation matrix is less than a size of the second transformation matrix. This can reduce a size of a transformation matrix used for calculating a correlation between position information, to reduce computing resource overheads of a model during inference or training.