Provided is a dynamic evolution modeling method for aluminum electrolysis process electrolytic bath technology energy consumption. The method is characterized by including the following steps of step 1, collecting data [XN, Y], step 2, carrying out normalization processing on the collected data, step 3, carrying out modeling on the data after the normalization processing by strongly tracking a square root trackless Kalman neural network, and step 4, estimating an electrolysis process energy consumption value by applying an established model to obtain a technology energy consumption value of the electrolysis process at the moment. The method has the advantages that advantages of strong tracking filtering and square root filtering are combined, convergence rates of the model and tracking ability on electrolytic bath mutation states are improved, the algorithm is stable, accuracy is high, tracking ability on the electrolytic bath mutation states is strong, therefore, real time estimation on the aluminum electrolysis process electrolytic bath technology energy consumption is achieved, technology operations on the aluminum electrolysis process can be optimized, and the purposes of saving energy and reducing emission can be achieved.