An adjustable contrast optical target equipment was constructed. After researching the relationship between image contrast and optical contrast, a contrast calibration method by the improved Back Propagation(BP) neural network was proposed. Firstly, the BP neural network model was designed for calibrating the contrast. Then, by combining the Levenberg-Marquardt(LM) with Shrinking-Magnifying Approach, the BP neural network was improved to optimize the convergence speed and generalization ability. Finally, based on the experimental platform of the adjustable-contrast target, the image contrast was obtained by measured radiation data. Comparing with the traditional BP algorithm, the improved one has a better convergence speed and generalization ability. Its calibration accuracy has been improved by 100 times and by 10 times as compared with those of the traditional BP network and the steepest descent method, respectively. When the training times is to be only 2 876 times, the maximum error between calibration value and target calibration value for the contrast is 0.01%, the training mean square error converges is 0.000 459 441, and the test error converges is 0.000 467 003. These results demonstrate that the algorithm is feasible and can meet the demands for contrast calibration in the equipment.