To increase the classification and recognition rate of sex hormones in a complex water environment, we proposed to combine three-dimensional fluorescence spectroscopy with a model of improved chicken swarm optimization based support vector machine (ICSO-SVM). An FS920 fluorescence spectrometer was used to analyzed the fluorescence characteristics of single-component solutions and mixed solutions of three typical sex hormones, i.e., estrone, estradiol, and estriol. On the premise of severe spectral overlap, the ICSO-SVM model was established to classify and identify the three sex hormones. The proposed model has stable training, fast convergence, and 100% sex hormone recognition rate for the test set, and thus it outperforms the PSO-SVM model. In conclusion, three-dimensional fluorescence spectroscopy combined with ICSO-SVM model is effective for sex hormone detection.