To improve the accuracy and applicability of the monocular depth estimation network with supervised learning for actual scene ranging tasks, a distance calculation method based on monocular depth estimation and calibration mechanism is proposed. Firstly, by introducing multivariate attention blocks and optimizing the design network structure, a network integrating global context and spatial attention mechanism (GSNet) is constructed. Then calibration parameters are formulated to establish the proportional relationship between the predicted distance of the scene and the actual distance of the scene, to obtain the calibrated distance value. Experimental results show that the fusion network GSNet and calibration mechanism can effectively reduce the error of the monocular depth estimation method in the actual measured distance. Compared with other methods, the average absolute error is less than 0.15m, and the average relative error of the measured distance in this method is less than 10%, which has good feasibility and accuracy.