In view of the traditional detection methods that cannot effectively detect small signals from the strong chaotic background noise, this paper studies the small target detection principle in the strong clutter background, and proposes a chaotic small signal detection method based on SSA-SVM. The sparrow search algorithm is used to optimize the penalty parameter C and kernel function parameter σ of SVM to improve the accuracy of prediction, thus reducing the detection threshold and increasing the detection rate. Adding target signals to Lorenz chaotic system for simulation, the results show that the proposed method can effectively detect small signals from strong chaotic background noise, and the root mean square error of prediction of transient small signals is 0. 000 434 3 (signal-to-noise ratio is -137. 707 3 dB), which is two orders of magnitude lower than the root mean square error of prediction signals of traditional SVM algorithm of 0. 049 (signal-to-noise ratio is -54. 60 dB). The proposed method is verified experimentally by using the sea clutter data measured by IPIX radar, which further demonstrates the effectiveness of the proposed method.