星系的红移在天文研究中极其重要, 星系测光红移的预测对研究宇宙大尺度结构及演变有着重要的研究意义。 利用斯隆巡天项目发布的SDSS DR13的150 000个星系的测光及光谱数据进行分析, 首先根据颜色特征并基于聚类的方法对星系进行分类, 由分类结果可知早型星系的占比较大。 对比了三种不同的机器学习算法对早型星系进行测光红移回归预测实验, 并找出最优的方法。 实验中将星系样本中u, g, r, i, z五个波段的测光值以及两两做差得到的10个颜色特征作为输入数据, 首先构建BP网络, 使用BP算法对星系的测光红移进行回归预测; 然后利用遗传算法（GA）优化BP网络各层参数, 将优化后的GA-BP算法应用于早型星系的回归预测试验中。 考虑到GA算法的复杂操作会影响预测效率, 并且粒子群算法（PSO）不仅稳定性高且操作简单, 因此将粒子群算法应用到星系样本中早型星系的测光红移回归预测实验中, 进而采用粒子群算法优化BP网络（PSO-BP）。 实验中将光谱红移作为期望值, 采用均方差（MSE）作为误差分析指标来评判三种算法的精度, 将PSO-BP回归预测结果与BP网络模型、 GA-BP网络模型进行比较。 由实验结果可知, BP网络的MSE值为0001 92, GA-BP网络的MSE值0001 728, PSO-BP网络的MSE值为0001 708。 实验结果表明, 所用到的PSO-BP优化模型在精度上优于BP神经网络模型和GA-BP神经网络模型, 分别提高了111%和12%; 在效率上优于传统的K近邻（KNN）测光红移估计算法, 克服了KNN算法中遍历所有数据样本进行训练的缺点并且其泛化性能优于其它BP网络优化模型。

In addition to the spectral redshift of galaxies, the prediction of galaxies redshift has important research significance for studying the large-scale structure and evolution of the universe. In this paper, we use the metering and spectral data of 150 000 galaxies of SDSS DR13 released by the Sloan Sky Survey project to analyze the galaxies according to the color characteristics and clustering methods. The classification results show that the early galaxies account for a large proportion. In this paper, three different machine learning algorithms are compared to measure the redshift regression prediction of early galaxies and find the optimal method. In the experiment, the photometric values of the galaxy samples u, g, r, i, z and the 10 color features obtained by the difference between the two bands are used as input data. First, the BP network is constructed, and the BP algorithm is used to measure the galaxies redshift. Then the Genetic Algorithm (GA) is used to optimize the parameters of the BP network, and the optimized GA-BP algorithm is applied to the regression prediction experiment of the early galaxies; considering the complex operation of the GA algorithm will affect the prediction efficiency. Moreover, the Particle Swarm Optimization algorithm not only has high stability and simple operation, so the Particle Swarm Optimization algorithm is used to optimize the BP network (PSO-BP) and Particle Swarm Optimization is used to optimize BP network (PSO-BP). By adjusting the weight method to improve the prediction efficiency and increase the stability, the particle swarm optimization algorithm is used to predict the redshift of the early galaxies in the galaxy samples. In the experiment, the spectral redshift is taken as the expected value, and the mean square error (MSE) is used as the error analysis index to judge the accuracy of the three algorithms. The PSO-BP regression prediction results are compared with the BP network model and the GA-BP network model. The experimental results show that the MSE value of the BP network is 0001 92, the MSE value of the GA-BP network is 0001 728, and the MSE value of the PSO-BP network is 0001 708. The experimental results show that the PSO-BP optimization model used in this paper is superior to the BP neural network model and the GA-BP neural network model in terms of accuracy, which is respectively improved by 111% and 12%. It is superior to the traditional K-nearest neighbor test in efficiency, which overcomes the shortcomings of traversing all data samples in KNN algorithm and its generalization performance is better than that of other BP network optimization models.