A similarity measurement method, comprising: obtaining a directional relationship between every two of n nodes in a network, and determining a transition matrix according to the directional relationships, wherein a dimension of the transition matrix is n × n, and n is a positive integer greater than or equal to 2; obtaining an attenuation factor, and calculating a constraint matrix according to the transition matrix and the attenuation factor using a processor, wherein the attenuation factor is an attenuation factor defined in a SimRank similarity method, and a dimension of the constraint matrix is n × n; wherein the constraint matrix is represented by A, a correction vector is represented by x, and the system of linear equations is represented by Ax = b, wherein b is a vector whose each element is 1; constructing a system of linear equations according to the constraint matrix, wherein a coefficient matrix of the system of linear equations is the constraint matrix, and a variable of the system of linear equations is a correction vector; solving the system of linear equations by means of iteration by using a Jacobi method, and determining the correction vector, comprises: calculating the correction vector by using x i k = 1 a ii b i − ∑ j ≠ i a ij x j k − 1, wherein x i represents an i th element of the correction vector x, x j represents a j th element of the correction vector x, a ij represents an element in an i th row and in a j th column of the constraint matrix A, a ii represents an element in the i th row and in an i th column of the constraint matrix A, b i = 1, k represents a quantity of times of iteration of the Jacobi method, i, j = 1, 2,···, n and k is a positive integer; generating a diagonal correction matrix according to the correction vector, wherein a diagonal element of the diagonal correction matrix is a component of the correction vector, and a dimension of the diagonal correction matrix is n × n; and calculating similarities between the n nodes according to the transition matrix, the attenuation factor, and the diagonal correction matrix. obtaining a directional relationship between every two of n nodes in a network, and determining a transition matrix according to the directional relationships, wherein a dimension of the transition matrix is n × n, and n is a positive integer greater than or equal to 2; obtaining an attenuation factor, and calculating a constraint matrix according to the transition matrix and the attenuation factor using a processor, wherein the attenuation factor is an attenuation factor defined in a SimRank similarity method, and a dimension of the constraint matrix is n × n; wherein the constraint matrix is represented by A, a correction vector is represented by x, and the system of linear equations is represented by Ax = b, wherein b is a vector whose each element is 1; constructing a system of linear equations according to the constraint matrix, wherein a coefficient matrix of the system of linear equations is the constraint matrix, and a variable of the system of linear equations is a correction vector; solving the system of linear equations by means of iteration by using a Jacobi method, and determining the correction vector, comprises: calculating the correction vector by using x i k = 1 a ii b i − ∑ j ≠ i a ij x j k − 1, wherein x i represents an i th element of the correction vector x, x j represents a j th element of the correction vector x, a ij represents an element in an i th row and in a j th column of the constraint matrix A, a ii represents an element in the i th row and in an i th column of the constraint matrix A, b i = 1, k represents a quantity of times of iteration of the Jacobi method, i, j = 1, 2,···, n and k is a positive integer; generating a diagonal correction matrix according to the correction vector, wherein a diagonal element of the diagonal correction matrix is a component of the correction vector, and a dimension of the diagonal correction matrix is n × n; and calculating similarities between the n nodes according to the transition matrix, the attenuation factor, and the diagonal correction matrix. calculating the correction vector by using x i k = 1 a ii b i − ∑ j ≠ i a ij x j k − 1, wherein x i represents an i th element of the correction vector x, x j represents a j th element of the correction vector x, a ij represents an element in an i th row and in a j th column of the constraint matrix A, a ii represents an element in the i th row and in an i th column of the constraint matrix A, b i = 1, k represents a quantity of times of iteration of the Jacobi method, i, j = 1, 2,···, n and k is a positive integer; generating a diagonal correction matrix according to the correction vector, wherein a diagonal element of the diagonal correction matrix is a component of the correction vector, and a dimension of the diagonal correction matrix is n × n; and calculating similarities between the n nodes according to the transition matrix, the attenuation factor, and the diagonal correction matrix.