Using AdaBoost algorithm of boosting learning to solve the suboptimal relays selection can reduce the realtime processing delay and computational complexity in cascaded relaying system, when wireless communication channels are in complex application scenarios such as multi-hop relays and sub-channel assignment. The channel state information of the legitimate channel and the eavesdropping channel is used as the input of the training model, and the index of the relay nodes with a certain value of the security capacity of the system is used as the output to transform the suboptimal relay selection problem of the cascaded relay system into a multiclass classification problem, which is solved by Support Vector Machines based on AdaBoost weighted voting. The suboptimal relay selection scheme for the cascaded relay system can be divided into three phases: generation of dataset, ensemble model training and result prediction. Finally, by plotting the classification accuracy and P-R curves, it is verified that the integrated learning model has higher accuracy for suboptimal relay selection and can improve the performance of relay collaboration.