Accurate and interpretable load forecasting is critical for modern power systems. Existing methods have made notable progress, however, with the development of smart grids, load data have become increasingly non-stationary under the influence of multiple factors. Further, model interpretability is subject to higher demands due to the need for regulatory compliance in power system operations. To address these challenges, we propose an interpretable forecasting model, Feature-weighted Liquid-core model with INterpretable Temporal attention (FLINT), that incorporates a feature-weighted strategy to dynamically assess the contribution of heterogeneous input features, leverages Liquid Neural Networks to capture the dynamic non-stationarity of load data, and integrates a time-aware attention mechanism to model temporal dependencies and highlight critical time steps. In addition, we innovatively introduce a multi-level interpretability module that, from global, local, and temporal perspectives, explains prediction outcomes by assessing input feature importance, tracing the causes of abrupt load changes, and highlighting critical time steps. Specifically, gradient attribution and gating quantify global feature contributions, the sparse, bio-inspired Liquid Neural Networks (LNNs) architecture enables traceable mutation-level reasoning, and attention highlights key temporal points. Empirically, a comparison with six baseline models on three real-world load datasets demonstrates that FLINT achieves approximately a 4% improvement in forecasting performance compared to the strongest baseline, as measured by MAE, while offering superior interpretability.