Abstract: Large-scale underground projects need accurate in-situ stress information, and the acoustic emission (AE) Kaiser effect method currently offers lower costs and streamlined procedures. In this method, the accuracy and speed of Kaiser point identification are important. Thus, this study aims to integrate chaos theory and machine learning for accurately and quickly identifying Kaiser points. An intelligent model of the identification of AE partitioned areas was established by phase space reconstruction (PSR), genetic algorithm (GA), and support vector machine (SVM). Then, the plots of model classification results were made to identify Kaiser points. We refer to this method of identifying Kaiser points as the partitioning plot method based on PSR–GA–SVM (PPPGS). The PSR–GA–SVM model demonstrated outstanding performance, which achieved a 94.37% accuracy rate on the test set, with other evaluation metrics also indicating exceptional performance. The PPPGS identified Kaiser points similar to the tangent-intersection method with greater accuracy. Furthermore, in the feature importance score of the classification model, the fractal dimension extracted by PSR ranked second after accumulated AE count, which confirmed its importance and reliability as a classification feature. The PPPGS was applied to in-situ stress measurement at a phosphate mine in Guizhou Weng’an, China, to validate its practicability, where it demonstrated good performance.