When diagnosing rotor bar faults using Motor Current Signature Analysis, the fault characteristic frequencies and amplitudes on both sides of the fundamental frequency are crucial parameters for determining whether a fault has occurred and its severity. The diagnostic capability of FFT algorithm heavily depends on the length of the analyzed data. Although the Least Squares Prony Analysis algorithm has short-time data analysis capabilities, it is highly sensitive to noise levels and suffer from insufficient fault feature extraction, and failure may occur when the motor operates at low frequencies and low loads. To address these issues, an improved method combining Singular Value Decomposition and LS-PA algorithms for diagnosing rotor bar faults is proposed. Initially, the SVD matrix is reconstructed using truncated data of original current signal, and effective order is determined based on the difference quotient of singular values. Subsequently, pre-processes technique is used to moderately suppress noise in stator current signal. And finally, the LS-PA algorithm is applied to identify and diagnose fault features from the preprocessed signal. Finite element simulation and experimental results demonstrate that the proposed method can effectively suppress signal noise and has the diagnostic performance of short-time data with high resolution. It achieves stable diagnosis of rotor bar faults under full load conditions, from light to full load, both in constant frequency and variable frequency power supply scenarios, outperforming traditional FFT methods.