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Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.传统神经网络采用梯度下降法训练网络结构，无法处理复杂优化问题。我们提出了一种改进型灰狼优化算法（SGWO），以探索更优的网络结构。通过环形种群初始化、信息交互机制与自适应位置更新对灰狼优化算法（GWO，Grey Wolf Optimizer）进行改进，以提升算法的搜索性能。将SGWO应用于埃尔曼（Elman）神经网络结构的优化，提出了一种新型预测方法SGWO-Elman。通过数学理论分析了SGWO的收敛性，并通过对比实验检验了SGWO的优化能力与SGWO-Elman的预测性能。实验结果表明：（1）SGWO的全局收敛概率为1，其迭代过程为一类带有吸收态的有限齐次马尔可夫链；（2）SGWO在求解不同维度的复杂函数时不仅具备更优异的优化性能，且将其应用于埃尔曼（Elman）神经网络的参数优化时，可显著优化网络结构，SGWO-Elman亦具备精准的预测性能。