In univariate time series classification tasks, effectively utilizing the multi-scale and time-dependent features of time series is crucial for enhancing classification accuracy. Addressing the limitations in existing models regarding the comprehensive use of multi-scale and time-dependent features, this paper introduces a new hybrid model MHAGRU-MCCE that combines the Multi-scale Conditional Convolution and Enhancement (MCCE) module with a Multi-Head Attention mechanism based GRU (MHAGRU). MCCE captures rich temporal features across different scales, while MHAGRU focuses on extracting the dependency relationships within the time series data. On 85 public datasets from UCR, comparative validation with six mainstream deep learning-based time series classification models, including MACNN, AFFNet, OS-CNN, LITETime, MLP, and LSTM-FCN, demonstrates that MHAGRU-MCCE achieves respective improvements in Mean Accuracy (MA) of 0.66%, 2.04%, 3.45%, 2.70%, 12%, and 2.89%. It also achieved the highest Arithmetic Mean Rank (AMR) = 2.45 and Geometric Mean Rank (GMR) = 1.98, fully demonstrating the effectiveness and superiority of MHAGRU-MCCE in handling univariate time series classification problems.