To further study the mapping relationship between narrowband hydroacoustic signal features and the direction of the arrival (DOA), an improved DOA estimation model combining regional Lipschitzs coefficients and local Lipschitzs coefficients is proposed based on the topological ordering of acoustic signal feature vectors based on three-layer self-organizing neural network mapping. This method is used to check the non-expansive mappings formed by the mapping of signal features to angles of arrival, which is a discussion of the regional Lipschitz coefficients as well as a judgment on the superiority of the mapping, using a self-organizing neural network as trainer, based on the topological ordering of feature layers, and combined with local Lipschitzs coefficients to construct an integrated DOA estimation law based on the 1-neighborhood-rules. The simulation experimental results shows that the method is effective in estimating the angle of direction of arrival，with the average error and variance within 10-2 degree; the estimation results also shows good robustness against other commonly used DOA estimation algorithms, when the signal-to-noise ratio (SNR) decreased from 20 dB to 2 dB.