Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of complex chemical systems. Central to the method is the potential energy surface (PES) that can describe the breaking and formation of chemical bonds. The development of both accurate and efficient PES has attracted significant effort in the past 2 decades. A recently developed deep potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD simulations. However, for complex chemical reaction processes like pyrolysis, it remains challenging to generate reliable DP models with an optimal training data set. In this work, a data set construction scheme for such a purpose was established. The employment of a concurrent learning algorithm allows us to maximize the exploration of the chemical space while minimizing the redundancy of the data set. This greatly reduces the cost of computational resources required for ab initio calculations. Based on this method, we constructed a data set for the pyrolysis of n-dodecane, which contains 35 496 structures. The reactive MD simulation with the DP model trained based on this data set revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation results are in good agreement with the experimental measurements. In addition, this data set shows excellent transferability to different long-chain alkanes. These results demonstrate the advantages of the proposed method for constructing training data sets for similar systems.