To address the problem that the electricity consumption behavior of market-based users was complex and variable, and the laws of electricity data were difficult to be accurately characterized, a market-based user electricity data fitting method considering behavioral characteristics was proposed. The *K*-means clustering algorithm was used to classify the electricity consumption behavior of customers and clarify the typical characteristics of each type of customers; the neural network model based on orthogonal polynomials was constructed, in which the neural network weight coefficients were trained by gradient descent algorithm and the orthogonal polynomials were Chebyshev polynomials, Hermite polynomials, Legendre polynomials and Laguerre polynomials. The simulation analysis was carried out using the electricity data of users in Jinan, Shandong Province, and four different orthogonal polynomials were used to fit the electricity data and calculate the evaluation indexes for different categories of users, so as to summarize the most suitable fitting methods for users with different behavioral characteristics. The simulation results showed that the power data fitting effect differed significantly among different implementation methods for similar users, and the fitting accuracy of the neural network models based on Hermite polynomials and Laguerre polynomials was relatively high, but the polynomial models with the highest power data fitting accuracy for different categories of users were different. Selecting the corresponding orthogonal polynomials to form a neural network fitting model according to the type of electricity consumption behavior was an effective way to achieve accurate fitting of user electricity data.