Abstract: Establishing a differentiated distribution model for a transformer can achieve a personalized evaluation of its operating status. However, the widely used online monitoring device has caused a rapid increase in the amount of data, which brings challenges to the construction of traditional distribution models. Problems such as computational interruptions and non-convergence of iterations occur in the parameter acquisition process. A fast calculation method for distribution characteristic parameters of dissolved gas data was proposed in this paper to fill this gap. Firstly, the reason for the abnormal calculation in the parameter acquisition process was analyzed. According to the massive dissolved gas data characteristics, an iterative initial calculation method based on the segmented least squares estimation method was proposed. Moreover, the optimal segment number selection method was proposed based on the calculation accuracy constraints. Verification test over real case shows that the proposed calculation method can not only solve abnormal situations in the parameter acquisition process of the distribution model, but also increase the calculation speed, and the calculation stability is better than traditional methods, which is suitable for processing millions of dissolved gas data in oil in the field. The proposed fast calculation method of distribution characteristic parameters proposed overcomes the limitations of traditional methods in the face of massive data and provides support for intelligent transformer operation and maintenance based on big data analysis.