A Phase Space Probabilistic Clustering Algorithm based on Multi-scale Quantum Harmonic Oscillator Algorithm (PSPCA-MQHOA) was proposed to solve the task scheduling and resource allocation of large clusters. Firstly, the cluster operating status was projected into the phase space, and the complex working state was transformed into the point set in the phase space. Furthermore, the phase space was meshed to form the Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) for discrete objective function. Finally, probabilistic clustering of cluster nodes was carried out by using the probability interpretation of wave function in the MQHOA process. PSPCA-MQHOA inherits the advantages of MQHOA, such as explicit physical model, strong search capabilities and accurate results, and it has few iterations due to the discretized phase space. Experimental results show that PSPCA-MQHOA can be applied to clusters in a variety of load conditions.