To address the low recognition rates due to the insufficient utilization of original signal timing information in automatic modulation recognition (AMR)， this paper proposes a signal pattern recognition algorithm based on frequency domain denoising and temporal convolutional networks （TCN）. Experiments are conducted using the standard dataset RML2016.10a， and a frequency domain denoising module （FDDM） is introduced to effectively suppress environmental noise. The I/Q components of the signal are converted into A/P components， followed by vector normalization to enhance stability. Finally， the preprocessed signals are fed into the TCN network for classification recognition. Results indicate that this algorithm achieves an average recognition rate higher than those of models such as gated recurrent unit （GRU）， convolutional long short-term memory （LSTM）， memory-cost-efficient convolutional neural network （MCNet）， cost-efficient hybrid deep learning network （CGDNet）， and denoising auto-encoder （DAE） when processing complex modulation schemes like 16 QAM and 64 QAM. Additionally， validation using actual I/Q data collected through the universal software radio peripheral （USRP） demonstrates that the algorithm exhibits good robustness and application potential under additive white Gaussian noise （AWGN） channels.