目的 为了有效克服BP神经网络算法权阈值随机选取造成的模型预测精度不高、结果输出不稳定的问题。方法 提出细菌群趋药性(BCC)算法和BP神经网络算法相结合的BCC-BP神经网络算法，采用BCC算法来选取BP神经网络初始权阈值，克服初始权阈值随机选取带来的问题，并将该算法应用到RGB到LAB色彩空间转换模型中。结果 按照国家普通印刷品的允许误差范围规定在6个标准色差单位以下的要求，在色差小于6的预测区间，基于BCC-BP算法的预测准确率达到81.07%，好于BP，GA-BP和PSO-BP算法，同时对于平均色差ΔE小于6个标准色差单位的要求，BCC-BP算法10次预测结果全部低于6。结论 采用BCC算法辅助BP神经网络进行初始权阈值的选取，可以有效提高BP神经网络模型在色彩空间转换应用中值的输出精度和稳定性。

This paper aims to effectively overcome the problem of low prediction accuracy and unstable output caused by the random selection of weight threshold of BP neural network algorithm. The BCC-BP neural network algorithm combining the bacterial colony chemotaxis (BCC) algorithm and BP neural network algorithm was proposed and the algorithm was applied to RGB to LAB color space conversion model. The BCC algorithm was used to select the initial weight threshold of the BP neural network to overcome the problems caused by the random selection of the initial weights and thresholds. According to the requirement that the allowable error range of national ordinary printed matter is below 6 standard chromatic aberration units, the prediction accuracy of BCC-BP algorithm is 81.07% when the chromatic aberration is less than 6, which is better than BP, GA-BP and PSO-BP algorithms. At the same time, the average chromatic aberration ΔE is less than 6 standard chromatic aberration units. The prediction results of BCC-BP algorithm for 10 times are all lower than 6. BCC algorithm is used to assist BP neural network to select the initial weights and thresholds, which can effectively improve the output accuracy and stability of the BP neural network model in the application of color space conversion.