To address the problems in the existing methods for radar signal feature recognition： the poor performance in low Signal-to-Noise Ratio （SNR） environments and the difficulty in handling multiple tasks of classification and parameter estimation simultaneously， a hybrid multi-task learning method fusing Convolutional Neural Network （CNN） and Transformer was proposed. In the method， multi-scale local feature extraction was integrated with global dependency modelling. To further achieve balanced joint optimization， a dynamic task weighting strategy fusing Bayesian uncertainty estimation and task-difficulty awareness was introduced to adjust the loss weights adaptively. Besides， an attention visualization module was developed to enhance model interpretability. Experimental results on the RadChar-Baseline dataset show that compared with baseline models such as IQST （IQ Signal Transformer）， CNN1D （One-dimensional Convolutional Neural Network）， CNN2D （Two-Dimensional Convolutional Neural Network）， ResNet1D （One-Dimensional Residual Network）， and Long Short-Term Memory （LSTM） network， the proposed method achieves a composite score of 0.931； within the SNR range of -20 dB to 20 dB， it has an average composite score of 0.937 with a standard deviation of only 0.108. It can be seen that the proposed method significantly outperforms the comparative methods in both classification and regression tasks， especially under challenging low SNR conditions.