Echocardiography is a critical imaging technique for diagnosing cardiac diseases, requiring accurate view recognition to support clinical analysis. Despite advancements in deep learning for automating this task, existing models face two major limitations: they support only a limited number of cardiac views, insufficient for complex cardiac diseases, and they inadequately handle out-of-distribution (OOD) samples, often misclassifying them into generic categories. To address these issues, we present EchoViewCLIP, a novel framework for fine-grained cardiac view recognition and OOD detection. Built on our collected large-scale dataset annotated with 38 standard views and OOD data, EchoViewCLIP integrates a Temporal-informed Multi-Instance Learning (TML) module to preserve temporal information and identify key frames, along with a Negation Semantic-Enhanced (NSE) Detector to effectively reject OOD views. Additionally, we introduce a quality assessment branch to evaluate the quality of detected in-distribution (ID) views, enhancing the reliability of echocardiographic analysis. Our model achieves 96.1% accuracy across 38 view recognition tasks. The code is available at https://github.com/xmed-lab/EchoViewCLIP .