The existing eye location algorithms have some disadvantages of single task and performance degrade in complex environment such as illumination， glasses and occlusion， so a multi- efficient， light-YOLO and lightweight neural network， MEL-YOLO， is designed for obtaining eye multi-attributes and landmarks. Based on the YOLOV3 network， combining with the enhanced DS-sandglass block， a denormalized coding and encoding method is used in the regression branch of key points to promote the network positioning depth， and the complete intersection-over-union （CIoU） and the mean square error （MSE） are introduced into the loss function， so promoting the overall performance of the network. On the near-infrared dataset， the MEL-YOLO network achieves the position accuracy of 100%， and achieves the attribute recognition rate and the landmark accuracy rate of 98.7% and 96.5%， while reaches 92% and 91% on the UBIRS dataset. The experimental results demonstrate that the MEL-YOLO network can accurately obtain eye multi-attributes and key point information. Also， it is proved that MEL-YOLO is small and robust， and has the firm generalization ability， thus applying to low-performance edge computing devices.