As the world's first geostationary satellite equipped with a passive microwave payload, China's FY-4M is planned to be launched at the end of 2026, ushering in a new era of continuous observation of various geophysical parameters associated with weather processes. To better understand the observational characteristics of this satellite’s more than a hundred channels, especially the potential application of its unique temperature hyperspectral channels (52.6–57.3 GHz) and several high-frequency channels in the high-precision detection of atmospheric temperature profiles over ocean, this paper proposes a complete retrieval algorithm with a channel optimization scheme, based on information entropy theory and Bayesian technique. Using degrees of freedom as an indicator, the ranking results of information contribution show that when hyperspectral channels are included, water vapor absorption channels and window channels used to obtain auxiliary information such as water vapor and hydrometeors are more important for the quantitative extraction of temperature profile information than traditional oxygen absorption channels at 50 GHz and 118 GHz. Based on this, a corresponding channel configuration was constructed for all-weather temperature profile retrieval. The results of retrieval experiments show that the root mean square error (RMSE) remains below 0.5 K under clear-sky and cloudy conditions, and is within 0.8 K during precipitation. Additionally, the computational time is reduced by 14 % relative to the full-channel configuration. This suggests that the presented algorithm with this channel configuration scheme is able to achieve a favorable balance between retrieval accuracy and computational efficiency, making it a preferred choice for future operational retrieval systems.