Avocado oil is a new vegetable oil extracted from avocado pulp. Because of its high price and limited public awareness, it is likely to produce and sell fake products. To meet the requirements of rapid, non-destructive, and high-throughput detection, this paper proposes a detection method for avocado oil by using a one-dimensional convolutional neural network model and in-situ micro Raman spectroscopy. The mixture of rapeseed oil and sunflower oil was used as the main component of avocado oil adulteration, and the spectra of pure vegetable oil and mixed oil were detected by in-situ micro Raman spectroscopy technology. The chemical information of the Raman spectrum characteristic peaks of avocado oil was analyzed and interpreted. The spectral information with synergy and correlation with Avocado concentration changes was selected through the covariance difference and correlation coefficient, and it was used as the input of the network model. The one-dimensional convolutional neural network model has a good prediction effect in the test set, with overall R²>0.915 and rmsr<0.0755. The detection method is based on a one-dimensional convolutional neural network model combined with in-situ micro Raman spectroscopy technology to predict the adulteration concentration of avocado oil, which is feasible and meets the detection requirements of market applications. The results have significant value for standardizing the domestic avocado market and accelerating the functional management of market supervision.