Abstract: Fixed step size Normalized LMS algorithm can’t step out the dilemma of fast convergence rate and low excess mean-square error. To solve this problem, this paper proposed a Segmental Variable Step-Size Normalized LMS algorithm Dependent on the state parameter of iteration coefficient. This Normalized LMS algorithm employs the state parameter of iteration coefficient to express the approximation degree between iteration coefficient and real coefficient. When the value of state parameter of iteration coefficient is larger than 1, which indicates that iteration coefficient tends to deviate from the real coefficient, at this time, variable step-size scheme that can provide larger step-size parameter is needed. However when the value of state parameter of iteration coefficient is smaller than 1, which indicates that iteration coefficient tends to approach the real coefficient, at this time, variable step-size scheme that can provide smaller step-size parameter is needed. This adaptive selection of variable step-size scheme enables the proposed NLMS algorithm to have better convergence performance. Analysis and experiment results show that: under the same experimental condition, the proposed algorithm can obtain faster convergence rate and lower excess mean-square error than other literatures.