Accurate segmentation of cardiac chamber structures in echocardiogram sequences is of great significance for clinical diagnosis and treatment. The imaging noise, artifacts, and the deformation and motion of the heart pose challenges to segmentation algorithms. Existing methods based on convolutional neural networks, Transformers, and space-time memory have indeed improved segmentation accuracy to some extent, but they are often restricted by limited local receptive fields and insufficient temporal memory retrieval.In this paper, we propose a novel model for echocardiography video segmentation, called GDKVM. The model employs linear key-value associations (LKVA) to effectively model inter-frame correlations, and introduces the gated delta rule (GDR) to ideally store intermediate memory states. The key-pixel feature fusion (KPFF) module is designed to integrate local and global features at multiple scales, enhancing robustness against boundary blurring and noise interference. We validated GDKVM on two mainstream echocardiogram video datasets (CAMUS and EchoNet-Dynamic) and compared it with various state-of-the-art methods. Experimental results show that GDKVM outperforms existing approaches in terms of segmentation accuracy and robustness, while ensuring real-time performance. GDKVM provides more accurate and efficient cardiac chamber segmentation outcomes for clinical applications.The code will be released upon publication.