In the present study, a total of 47 levofloxacin hydrochloride injection samples were detected by near infrared (NIR) spectroscopy, and 37 samples were randomly selected to establish the quantitative models by partial least squares (PLS) and artificial neural network (ANN) technology, while other 12 samples were used for prediction. On the one hand, the model was established by PLS, the coefficient of determination (R^2) of the prediction is 0. 964, and the root mean squared error of prediction (RMSEP) is 0. 242 8. On the other hand, after the spectrum variables were highly effectively compressed using the wavelet transformation technology, the quantitative analysis model of levofloxaein hydrochloride was established through the ANN technology. The R^2 and RMSEP of the model is 0. 944 and 0. 572 2, respectively. In this work, we have a detailed comparison between the two technologies in the progress of two quantitative models and optimizing correlative parameter, and finally we got a satisfied result. The simulation experiment indicated that the above PLS model is more steady and precise than ANN model, which can get hold of a rapid and nondestructive quantitative analysis result of the injection. Thus, the research can provide powerful scientific basis and technical support for further analysis of levofloxacin hydrochloride injection.