The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit the complex information within spectra, this study proposes a novel strategy for estimating citrus LWC by integrating spectral preprocessing combinations with an enhanced deep learning architecture. Utilizing a citrus plantation in Guangxi as the experimental site, 240 leaf samples were collected. Seven preprocessing combinations were constructed based on multiplicative scatter correction (MSC), continuous wavelet transform (CWT), and first derivative (1st D), and a new multichannel network, EDPNet (Ensemble Data Preprocessing Network), was designed for modelling. Furthermore, this study incorporated an attention mechanism within EDPNet, comparing the applicability of SE Block, SAM, and CBAM in integrating spectral combination information. The experiments demonstrated that (1) the triple preprocessing combination (MSC + CWT + 1st D) significantly enhanced model performance, with the prediction set R² reaching 0.8036, a 13.86% improvement over single preprocessing methods, and the RMSE reduced to 2.3835; (2) EDPNet, through its multichannel parallel convolution and shallow structure design, avoids excessive network depth while effectively enhancing predictive performance, achieving a prediction accuracy (R² = 0.8036) that was 5.58–9.21% higher than that of AlexNet, VGGNet, and LeNet-5, with the RMSE reduced by 9.35–14.65%; and (3) the introduction of the hybrid attention mechanism CBAM further optimized feature weight allocation, increasing the model’s R² to 0.8430 and reducing the RMSE to 2.1311, with accuracy improvements of 2.08–2.36% over other attention modules (SE, SAM). This study provides a highly efficient and accurate new method for monitoring citrus water content, offering practical significance for intelligent orchard management and optimal resource allocation.