A novel wavelength selection algorithm, based on wave cluster interval, for infrared spectroscopy in the detection of volatile organic gases is presented. The algorithm employs a series selection mode, utilizing characteristic wavelength point cluster classification and absorption peak interval screening. To begin with, cluster analysis is conducted to retain prominent absorption peak features while minimizing the potential for algorithmic over splitting or random uncertainty in wavelength intervals. Subsequently, an improved moving window method is devised, and a greedy algorithm is employed to re-screen wavelength points within the same cluster class. This process ensures the retention of the optimal wavelength range, crucial for representing spectral characteristics and facilitating subsequent model predictions. Experimental validation was conducted using infrared spectral data of styrene, para-xylene, and o-xylene, employing four models: partial least squares, ridge regression, support vector machine. The results demonstrate that, while maintaining model accuracy, the dataset can be reduced to 43.71%~36.35% of its original size. Additionally, utilizing a dataset comprising three gases (two concentrations each), as well as fully arranged and combined mixed gases, we conducted comparative experiments on three different CNN structures. The effectiveness of the proposed algorithm in reducing machine learning model complexity while ensuring prediction accuracy was validated through experimental comparisons before and after spectral waveform selection, with the CNN prediction models demonstrating a 90% increase in operational efficiency post-wavelength selection.