Deep learning offers a new path for tropical cyclone forecasting， yet its black-box characteristic hinders its application in critical fields. Therefore， a review was conducted on the application research of explainable deep learning in tropical cyclone forecasting. First， the latest advances of deep learning research on tropical cyclone forecasting were classified systematically. Second， the advantages and disadvantages of traditional methods of forecasting were reviewed. Third， attention is focused on the applications of Convolutional Neural Network （CNN）， Recurrent Neural Network （RNN）， Long Short-Term Memory （LSTM） Network， and Transformer for forecasting tasks， and the frontier explorations of methods such as Generative Adversarial Network （GAN） were discussed. On this basis， the importance of explainable deep learning in tropical cyclone forecasting was analyzed in a deep way， the specific application of explainable deep learning to tropical cyclone forecasting was explored in three dimensions： feature importance interpretation， spatio-temporal mode interpretation， and uncertainty quantification， and its potential in enhancing model transparency and user confidence was pointed out. Finally， to address current challenges faced by explainable research， future research prospects of combining physical mechanisms， multi-granularity visualization， and causal inference were introduced.