<p>The quay crane operates in a severe and specific working environment. The gearbox, being a pivotal transmission component within the hoisting mechanism, plays a crucial role in ensuring the reliable and smooth operation of the entire system. Its health status is of utmost importance. To derive degradation characteristics from intricate vibration monitoring signals, we introduce an enhanced KW symbol entropy feature extraction approach. This method ensures uniformity in symbol standardization by adopting the root mean square (RMS) of the signal under normal conditions as the benchmark, integrating a symbol coefficient to establish a uniform symbol scale. Additionally, we incorporate a variable number of symbols to enlarge the symbol set, thereby enhancing the capacity for information representation. Subsequently, leveraging information entropy theory, we compute the complexity of both the symbol sequence itself and its distribution, yielding two distinctive features: improved symbol sequence entropy (IKSE) and improved symbol distribution entropy (IKDE). We have analyzed the logistic chaotic sequence and the operational lifespan signal of the hoisting gearbox independently. The results demonstrate that the introduced features possess remarkable capabilities in elucidating the complexities embedded within nonlinear time series, thereby facilitating a precise depiction of the entire progression of performance degradation in the lifting gearbox. Notably, this technique manifests a remarkable degree of stability, with its performance remaining largely unaffected by variations in its parameters.</p>