Conventional wireless local area network intrusion signal detection nodes are mostly set independently, resulting in low detection efficiency and high false detection rate of intrusion signal detection. Therefore, this article proposes a wireless local area network intrusion signal detection method based on GA-SVM algorithm. This method first uses correlation to extract intrusion sig‐ nal features, improve detection efficiency, set up correlation detection nodes, construct a GA-SVM intrusion signal detection model, and use location separation method to achieve signal detection processing. The test results show that for signal intrusion detection at the selected 300 sampling points, compared to the traditional distributed fiber optic network intrusion signal detection group and the traditional FastICA calculation network intrusion signal detection group, the GA-SVM calculation network intrusion signal detection group has a good detection error rate of less than 20%, indicating that with the assistance of the GA-SVM algorithm, the current de‐ sign has better detection results, stronger pertinence, and practical application value.