Abstract: Seismic waves, particularly their high-frequency components, will undergo energy attenuation during subsurface propagation. This results in a low signal-to-noise ratio (SNR) within the high-frequency band, significantly reducing the accuracy of seismic exploration for deep oil and gas. To enhance the SNR of deep seismic data, this study employed the time-frequency analysis technique to describe the time variations of signal frequency based on the non-stationary characteristics of seismic signals. Accordingly, this study proposed a method for suppressing deep high-frequency noise based on time-frequency analysis. Considering the stable and similar time-frequency characteristics of effective signals and the uncertain and random high-frequency interference, this study proposed an adaptive threshold selection strategy based on correlation analysis. This strategy involves extracting the time-frequency characteristics of effective signals from an advantageous frequency band and comparing them with the time-frequency characteristics within the high-frequency band. Subsequently, feature-constrained attenuation was applied to time-frequency spectra in the high-frequency band, thereby suppressing high-frequency noise. Theoretical models and actual data processing results demonstrate that the proposed method can effectively suppress high-frequency noise and significantly enhance the SNR of deep seismic data.