Echocardiographic view classification is essential for accurate cardiac assessments, yet it remains challenging due to anatomical overlap, operator variability, motion artifacts, image quality issues, and dataset limitations. Deep learning methods could address these issues by incorporating temporal models, representation learning, and domain adaptation to improve classification robustness. This study proposes a contrastive representation learning framework that integrates temporal and spatial augmentation strategies, to learn more robust and invariant feature representations. Experimental results demonstrate that the proposed approach achieves an accuracy of 96.4%, surpassing previous methods. The findings indicate that the model effectively captures robust and invariant feature representations, strengthening its ability to distinguish between echocardiographic views and consequently enhancing classification performance.