Eating and broadcasting's "new favorite" - wax bottle sugar has become a popular food for young people. Wax clothes are not easy for the human body to digest, and there may be a risk of industrial wax. In response to the potential industrial wax contamination of wax bottle sugar shells, this study conducted a quantitative analysis of illegal additives in wax bottle sugar using Raman spectroscopy technology combined with backpropagation neural network (BPNN) algorithm. The Raman spectra of natural beeswax and paraffin wax were detected, and the similarities and differences between the two Raman spectra were compared. The molecular bonds and vibration modes corresponding to the characteristic peaks of the Raman spectra were analyzed and determined. The paper simulated the research work on the natural beeswax and paraffin wax mixture and compared several linear and nonlinear correction methods. It was found that the combination of BPNN algorithm and Raman spectroscopy technology is the optimal method for detecting the mixture of natural beeswax and paraffin wax. The model also predicted the potential paraffin wax in online wax bottle sugar. This study proposes a detection technique based on Raman spectroscopy combined with BPNN algorithm to address wax bottle sugar's potential food safety issues. This technique has specific reference significance for strengthening the supervision and guidance of wax bottle sugar.