The feature of the slice of ambiguity function main ridge can better reflect the structural essential differences between signals， and it is a feasible parameter to solve the current complex system radar emitter signal sorting problem. The fast and intelligent search for the slice of ambiguity function main ridge is an important issue to increase the practicability of its feature of the slice. In this paper， an improved self-adaptive grey wolf optimization （GWO） combining uniform initialization strategy and improved nonlinear convergence factor was proposed to search the main ridge slice of ambiguity function of six typical radar emitter signals and extract the feature of slices， which were compared with the exhaustive method and standard GWO.The experimental results showed that the average time consumption of the proposed method was only 1.49 s when searching AFMR slice and extracting feature. Compared with the exhaustive method and the standard GWO， the efficiency was improved by 75.7% and 19.0%， respectively， with better timeliness. In a fixed SNR environment， when the SNR was not less than 0 dB， the average clustering accuracy of the extracted feature values was 96.4%； and in a dynamic SNR environment of 0—20 dB， the average clustering accuracy can reach 95.2%， with good accuracy， anti-noise performance and strong intra-class aggregation and inter-class separation ability， which proves the feasibility and effectiveness of the proposed method.