Objective To study the method for prediction accuracy of color space conversion of monitor in BP neural network optimized based on particle swarm optimization. Methods The weights and threshold of the BP neural network were mainly optimized through the improvement of data normalization and maximum limiting speed, inertia constant and the fitness function, in order to reduce their distribution range, and predict the color difference through the BP neural network method. Results Through improved particle swarm optimization algorithm of BP neural network prediction model, after 20 times of tests, the average color difference reached 2. 8526, and the minimum average color difference reached 2. 0453. Conclusion The results showed that the method could greatly reduce the probability of BP neural network prediction model falling into local minima, resulting in good non-linear fitting capability and higher prediction accuracy in color space conversion.