Abstract: To address the difficulty of accurately characterizing the state evolution of key equipment in complex ship systems under complex sea conditions and multi-operating modes, this paper proposes an equipment state prediction method based on Single-Feature Independent Modeling (SFIM) and adaptive parameter optimization. From an equipment-level modeling perspective, independent models are established for state features with different physical properties within the navigation task channel. This approach effectively avoids the degradation in prediction accuracy caused by feature interference in multi-feature joint modeling, enabling the model to concentrate on the temporal evolution patterns of individual features. On this basis, the Particle Swarm Optimization (PSO) algorithm is introduced to adaptively optimize the key hyper-parameters of the Long Short-Term Memory (LSTM) network, ensuring that the model configuration matches the temporal variation characteristics of different equipment state features. Taking representative equipment state features within the navigation task channel, such as vibration, temperature, noise, and deformation, as research objects, prediction modeling and comparative analysis are conducted based on ship model test data. Experimental results show that the proposed PSO −SFIM −LSTM model achieves high prediction accuracy across multiple feature prediction tasks. Specifically, in temperature feature prediction, its mean squared error (MSE) is reduced by more than one order of magnitude compared with the GRU and CNN-1D models. For vibration and noise feature prediction, the prediction error is reduced by approximately 20%–40%, demonstrating better prediction stability and feature adaptability. Furthermore, the proposed model is applied to the analysis of operational data from ship equipment. The results show that, as the equipment state transitions from stable operation to abnormal conditions, the prediction curve exhibits a trend deviation prior to the onset of significant changes in the actual state. This provides an effective basis for the early identification of potential equipment faults and operational and maintenance decision-making.