Based on our original development of a new laser absorption spectroscopy chamber (LASC) system, we further report ammonia emission concentration measurement using the LASC system based on deep belief networks (DBNs), aiming to present an effective approach for the retrieval of the gas concentration to increase the measurement accuracy of the LASC system and expand its application to monitoring ammonia emission in farmland. Surrounding the LASC system, an experimental system was constructed, and a DBN algorithm was introduced for gas concentration retrieval. The absorption spectroscopy obtained by the experimental system was first pretreated by an empirical wavelet transform algorithm and principal component analysis method, which greatly improved the signal-to-noise ratio of the signal and reduced the dimensionality of the processed signal to meet the need of the training of DBN model. The results showed that the measured gas concentrations were close to true values with small errors, and the mean relative error obtained by the DBN algorithm (0.37%) was much smaller than those obtained by the back-propagation neural network algorithm (0.97%) and absorbance peak method (2.37%) in a wide range of NH3 standard concentrations. Field experiments verified the effectiveness and reliability of the LASC system when it was applied to ammonia emission measurement in farmland with the concentration retrieval based on the DBN algorithm, which is of importance for its applications in air pollution detection.