Clothing-changing person re-identification (CC-ReID) aims to address cross-camera person identification challenges caused by variations in pedestrian attire, making it a highly valuable research area within computer vision. Due to the high cost of labeling data, unsupervised learning methods have gained significant attention for CC-ReID tasks. However, existing unsupervised methods frequently suffer from high pseudo-label noise and reliance on complex preprocessing (e.g., human parsing) or multi-encoder architectures, resulting in increased computational overhead and deployment difficulties. To tackle these challenges, this paper proposes an end-to-end unsupervised CC-ReID framework named Multi-Scale Adaptive Clustering and Local Consistency Learning (MALC). Utilizing a single CLIP Vision-Encoder as its backbone, MALC discards such intricate procedures. Its core innovations include a multi-scale adaptive density clustering (MS-ADC) strategy to improve pseudo-label quality, and a local consistency learning (LCL) approach that imposes constraints on local region features to enhance robustness against clothing variations. Through the joint optimization of global and local losses, the model learns a highly discriminative and robust feature representation. Experimental results demonstrate that MALC, employing only RGB images, substantially outperforms comparable unsupervised approaches that rely on additional parsing information, showcasing notable advantages in identification accuracy and ease of deployment. The code will be made available at: https://github.com/ykding666/MALC.