Sequential recommendation systems play an increasingly important role in scenarios such as e-commerce and social media. Traditional sequential recommendation methods often assume that user behaviors are entirely driven by stable preferences, neglecting external perturbation biases caused by latent temporal factors within behavior sequences. In practice, user actions are shaped by long-term preferences together with time-related factors such as seasonal variations, which give rise to phase-specific demand shifts. Directly using such perturbed behaviors for preference modeling can result in preference drift and overfitting to short-term behaviors. To address these challenges, this paper proposes Time-varying Demand Causal modeling for Recommendation debiasing (TDCRec). The framework first identifies demand as a confounding factor causing spurious correlations in recommendations through a causal graph, models user demand, and obtains debiased recommendation results via backdoor adjustment. It then introduces a user demand stability metric and employs an adaptive fusion mechanism to dynamically fuse base recommendation and intervened recommendation, enabling personalized recommendation inference. We validate the effectiveness of TDCRec on three public datasets.