ArF excimer lasers with short wavelength and high photon energy are widely applied in the field of integrated circuit lithography, material processing, laser medicine, and so on. Excimer laser single pulse energy is a very important parameter in the application. In order to detect the single pulse energy on-line, one energy detector based on photodiode was designed. The signal processing circuit connected to the photodiode was designed so that the signal obtained by the photodiode was amplified and the pulse width was broadened. The amplified signal was acquired by a data acquisition card and stored in the computer for subsequent data processing. The peak of the pulse signal is used to characterize the single pulse energy of ArF excimer laser. In every condition of deferent pulse energy value levels, a series of data about laser pulses energy were acquired synchronously using the Ophir energy meter and the energy detector. A data set about the relationship between laser pulse energy and the peak of the pulse signal was acquired. Then, by using the data acquired, a model characterizing the functional relationship between the energy value and the peak value of the pulse was trained based on an algorithm of machine learning, Support Vector Regression (SVR). By using the model, the energy value can be obtained directly from the energy detector designed in this project. The result shows that the relative error between the energy obtained by the energy detector and by the Ophir energy meter is less than 2%.