This paper establishes a data-driven transmission line lightning fault classification and diagnosis model

proposes an improved sparrow search algorithm to optimize the parameters of the support vector machine

and realizes the transmission line lightning fault classification and diagnosis. First

the adaptive t-distribution algorithm is used to improve the sparrow search algorithm

which overcomes the shortcomings of the sparrow search algorithm that is easy to fall into the local optimum

thereby improving the optimization ability of the sparrow search method. Then the improved algorithm is used to optimize the SVM parameters to improve the classification accuracy. Finally

the transmission line fault analysis and lightning location system data are used

and the key fault characteristics are selected to form a sample data set

and the tSSA-SVM is used to construct the transmission line fault diagnosis and classification model. Case analysis shows that the model can effectively realize the lightning fault diagnosis and classification of transmission lines based on data analysis. The accuracy of the proposed algorithm in fault diagnosis classification is higher than that of the classic SVM

and the algorithm efficiency is superior to fault diagnosis and classification algorithm based on the PSO-SVM（particle swarm optimization-support vector machine）.