Under the background of the new power system, the efficient aggregation and deployment of demand-side flexibility resources through virtual power plants is a beneficial measure to improve the flexibility of the new power system. Air-conditioning load is one of the most potential demand-side resources, and its accurate modeling can lay the foundation for the design of control strategy. Aiming at the problems of limited fitting accuracy, ignoring environmental factors and low efficiency of current air-conditioning system parameter identification, in order to further accurately quantify the scheduling potential of air-conditioning load in engineering applications, this paper proposes an improved Newton-Raphson-based optimizer ( INRBO ) based air-conditioning second-order equivalent thermal parameters ( ETP ) analytical solution dynamic parameter identification method. Firstly, in order to reduce the inherent cumulative error due to the traditional second-order recursive form parameter identification method, a second-order equivalent thermal parameter model based on analytical solution is introduced. Secondly, considering the multi-parameter dynamic characteristics caused by variable factors such as window ventilation, personnel flow and temperature sudden change, the second-order ETP parameter identification model of air conditioning considering multi-parameter dynamic characteristics is established with the minimum dynamic error between the simulation system and the actual system as the optimization goal, taking into account the initial solid temperature in the room. Finally, in order to improve the solution efficiency, an improved Newton-Raphson optimization algorithm based on Chebyshev chaotic mapping is proposed to solve the above model. The results of the example show that the dynamic parameter identification model established in this paper can take into account the fitting accuracy and solution efficiency.