In cognitive radio systems, to address the problem of inaccurate noise power estimation in traditional energy detection algorithms, a spectrum sensing optimization approach based on dynamic sub-band partitioning is proposed. Two methods are designed in this study. The first method employs the fluctuation degree of the signal envelope to divide the spectrum into multiple sub-bands and introduces an adaptive threshold coefficient to form an adaptive spectrum sensing scheme. The second method utilizes a sliding window to dynamically partition sub-bands and incorporates a single optimal threshold detection mechanism, referred to as the optimal threshold method, to achieve enhanced sub-band division and spectrum sensing.Simulation results demonstrate that, compared with the conventional single optimal threshold detection, the proposed optimal threshold method achieves up to a 10% improvement in detection performance within the SNR range of -11.7 to 0 dB, and exhibits significantly higher detection probability than the adaptive spectrum sensing method over the range of -20 to 0 dB. Overall, the optimal threshold method shows superior spectrum sensing capability and robustness.