Wideband oscillations pose severe threat to the safe and stable operation of power systems. To address this issue, a wideband oscillation monitoring method based on deep residual network (ResNet) and improved temporal convolutional neural network (ITCN) is proposed. First, the ResNet structure is used to convolve wideband oscillation signals, capturing adjacent local features of the time series through sliding windows. The multi-scale features of the oscillation signals are extracted and compressed by stacking the residual blocks. Then, the ITCN structure applies dilated causal convolutions to expand the compressed features, introducing progressively larger receptive fields while maintaining computational efficiency. This enables further extraction of medium- and long-term dependencies in the time series, and the combination of both networks facilitates comprehensive global feature extraction. Finally, an attention mechanism is embedded into the TCN structure to assign adaptive weights to important signal features, thereby improving the capture of global patterns and long-term dependencies. Simulation and real-world measurements verify that the ResNet-ITCN model can successfully detect wideband oscillation parameters and identify oscillation types, achieving effective wideband oscillation monitoring.