Contrast-enhanced ultrasound （CEUS） is a powerful diagnostic tool that enhances blood flow signals from tumor micro-vessels through the peripheral venous injection of ultrasound contrast agents. This enables clinical physicians to dynamically evaluate tumor angiogenesis in real-time. CEUS imaging is widely used for the diagnosis， postoperative evaluation， and treatment planning of multiple organs. In recent years， deep learning techniques have made considerable progress， offering new opportunities for the intelligent analysis of dynamic CEUS. Deep learning methods have widened the scope of clinical applications largely， improving its efficacy of diagnosis and treatment. However， similar to the traditional ultrasound imaging， CEUS is faced with the challenges of interference from speckle noise， respiratory motion， and low standardization， making the analysis of spatial-temporal information of dynamic perfusion become difficult. This paper systematically reviews recent research on the intelligent analysis of CEUS， covering clinical applications such as benign-malignant differentiation， malignant grading， therapeutic prediction， and the selection of diagnosis and treatment plans. We summarize the latest advances of radiomic and deep learning methods in the area of CEUS imaging analysis， and highlight the limitations of current research and future directions for development.