Underdetermined blind source separation (UBSS) is a difficult problem in the field of blind signal processing, and the mixed matrix estimation is a key step in determining the success or failure of over-complete independent component analysis (ICA) and underdetermined blind separation. In order to improve the estimation accuracy of the underdetermined mixed matrix, a linear clustering analysis method for optimization of artificial bee colony search strategy is proposed based on single-source-point detection. Firstly, the observed signals in the time domain are transformed into the time-frequency domain using short-time Fourier transform (STFT), and single-source-point (SSP) detection is performed to enhance the linear clustering characteristics of the signal. Then, based on sparse time-frequency signals, the bee colony food source is matrix coded to make the artificial bee colony algorithm fit seamlessly with the blind separation problem. Randomness and deterministic search strategies are combined to coordinate the diversity of bee colonies with the convergence rate of clustering algorithm. Levy flight strategy is introduced into the local search of bee colonies to further explore the neighborhood of the current optimal solution to improve the clustering accuracy. Finally, the column vectors of the mixed matrix are estimated using the straight line direction vectors of the linear clustering generated by the artificial bee colony algorithm. The simulation results of audio signals show that the improved artificial bee colony search strategy proposed in this paper can not only provide effective linear clustering analysis, but also improve the estimation accuracy of underdetermined mixing matrix.