The accurate detection of chlorine leakage in an open environment has been an urgent problem for chlor-alkali manufacturers. Differential optical absorption spectroscopy (DOAS) can realize long-distance measurements of trace polluting gases in the atmosphere. Due to the flat characteristic of the UV absorption spectrum of chlorine, it is impossible to differentiate the absorption characteristics from the noise signal by normal methods. A new algorithm based on a one-dimensional convolutional neural network (1D-CNN) is proposed to solve the problem of poor accuracy caused by noise interference, which can fully use spectral information and extract chlorine absorption characteristics layer by layer. Compared with commonly used models such as least squares (LS), multilayer perceptron (MLP), support vector machine (SVR), and k-nearest neighbor (KNN), the inversion result of this algorithm has the highest accuracy (R²=0.996, RMSE=4.40, MAE=2.64, SMAPE=8.51%). Due to the inevitable random noise in -the system, the preprocessing effects of the S-G filter, Fourier transform, singular value decomposition, and wavelet transform decomposition algorithms are compared. The results show that S-G filtering and wavelet decomposition algorithms can retain the characteristic information of chlorine while removing noise and further improving the model’s performance. The concentration inversion model based on 1D-CNN provides a new feasible method for long-distance quantitative detection of chlorine leakage in the open environment.