More than 230 thousand medical personnel worldwide have been infected with the novel coronavirus since the outbreak of COVID-19. Several medical professionals have praised the nonartificially contacted dorsal hand vein automatic injection method owing to its high isolation. The key for realizing no-contact automatic injection of dorsal hand veins is to realize the detection and segmentation of dorsal hand veins and determine the needlepoint position. In this study, an image processing algorithm based on improved U-Net with guidance and attention mechanism (AT-U-Net) is proposed to detect the dorsal hand vein. The proposed method was validated using a self-built dorsal hand vein database and the results indicate that it performs well with the accuracy of 93.6%. Following the detection of the dorsal hand vein, this study proposes a needle entry point location determination method for dorsal hand veins based on an improved pruning algorithm (PT-Pruning). The trunk line of the dorsal hand vein was extracted via PT-Pruning. The optimal injection point of the dorsal hand vein is determined by considering the vascular cross-sectional area and the bending value of each venous vein injection point area. Compared to the self-built dorsal hand vein injection point database, the detection accuracy of the injection area at the effective injection point is 96.73%, while the detection accuracy of the injection area at the optimal needle entry point is 96.5%. Thus this study lays the groundwork for subsequent mechanical automatic injection.