The invention relates to a mining method of computer data for recommendation systems and belongs to the technical field of computer data processing. The method includes that user preference matrixes and service project preference matrixes are initialized in a main server of a computer, row vectors of preference matrixes input by a user are distributed to a plurality of mappers in the computer, all mappers calculate sub-directions of gradient directions of the user preference matrixes and the service project preference matrixes respectively, calculated results are sent to a simplifier in the computer, the simplifier receives and accumulating the sub-directions of the gradient directions, and the user preference matrixes and the service project preference matrixes are updated according to gradient direction matrixes of the user preference matrixes and the service project preference matrixes. According to the method, the existing probabilistic matrix factorization (PMF) algorithm is modified, large-scale data processing capabilities are improved; and preference matrixes are stored by data storage structures of key-value pairs, so that occupied storage space is small, and reading speed of data are rapid.