根据鄂西某输电线路拉线塔的拉线棒腐蚀数据，通过灰色关联度算法分析了土壤因素与拉线棒腐蚀情况之间的相关性；应用粒子群算法（PSO）对最小二乘支持向量机（LSSVM）的关键参数进行优化；利用灰色关联度权重对有关数据进行处理，建立了PSO-LSSVM和考虑灰色关联度权重的PSO-LSSVM预测模型。实例计算表明，与LSSVM预测模型相比，PSO-LSSVM预测模型训练集所得结果的均方根误差下降了15.3%；预测集的均方根误差下降了35.71%。考虑灰色关联度权重后，PSO-LSSVM预测模型训练集和预测集的均方根误差进一步下降，分别减少了24.59%和20%。PSO-LSSVM用于拉线棒腐蚀预测具有较好的精度，考虑灰色关联度权重的PSO-LSSVM模型的预测精度更高。

According to the corrosion data of the anchor rod of a transmission line tower in western Hubei, the correlation between soil factor and rod corrosion was analyzed by grey relational degree algorithm. The key parameters of least squares support vector machine (LSSVM) were optimized by particle swarm algorithm (PSO), and the data were processed by the grey correlation degree weights. The PSO-LSSVM prediction model and the PSO-LSSVM prediction model considering the grey correlation degree weights were established separately. The example showed that the mean square error of the PSO-LSSVM model training set was decreased by 15.3% and the mean square error of the prediction set was decreased by 35.71% compared with the LSSVM model. When the weight of grey correlation degree was considered, the mean square error of the training set and the predictive set in the PSO-LSSVM prediction model decreased further, and the value reduced 24.59% and 20% respectively. The PSO-LSSVM was used to predict the corrosion of anchor rods with relatively good precision, and the PSO-LSSVM prediction model considering the grey correlation degree weights was more accurate.