<p>High-dimensional data with left-censored responses are increasingly common in modern applications, yet existing methods for analyzing them are limited. Classical Tobit models fail to handle nonlinear relationships or perform high-dimensional variable selection, whereas deep learning approaches often prioritize prediction performance but lack selection and interpretation capabilities. To address this gap, we propose an integrated deep learning framework, the Deep Tobit model, which employs the negative Tobit log-likelihood as its loss function to properly account for data censoring. A two-stage feature selection algorithm is further developed, with theoretical guarantees on convergence rate and selection consistency. Extensive simulation studies and real-data applications on left-censored aero-engine casing vibration data and HIV viral load data demonstrate that the proposed framework outperforms several state-of-the-art baselines in both variable selection and prediction accuracy.</p>