Building integrated energy systems (BIESs) are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption. Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power (CHP) units. To this end, this paper proposes a soft actor-critic (SAC) algorithm to solve the scheduling problem of BIES, which overcomes the model non-convexity and shows advantages in robustness and generalization. This paper also adopts a temporal fusion transformer (TFT) to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand. The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps. Furthermore, its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm. The proposed hybrid data-driven approach integrating TFT and SAC algorithm, i.e., TFT-SAC approach, is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches. The generalization performance for the scheduling policy, as well as the sensitivity analysis, are examined in the case studies.