When using traditional Mel-frequency cepstral coefficients (MFCC) as features for target recognition in wild filed environments, their sensitivity to environmental interference often leads to a decline in recognition accuracy. To address this issue, this study proposes replacing the discrete cosine transform used in the MFCC extraction process with principal component analysis (PCA) and applying a nonlinear function to map the logarithmic energy obtained after Mel filtering. The improved MFCC are more aligned with actual data, can enhance the weighting of frequency bands containing the target′s signal, and have better separability and robustness. Experimental results show that using the improved MFCC based on PCA and nonlinear mapping as classification features achieves an accuracy of 93.36%, surpassing the performance of traditional MFCC.