Abstract Nanoscale materials characterization often uses highly energetic probes which can rapidly damage beam-sensitive materials, such as hybrid organic–inorganic compounds. Reducing the probe dose minimizes the damage, but often at the cost of lower signal-to-noise ratio (SNR) in the acquired data. This work reports the optimization and validation of principal component analysis (PCA) and nonnegative matrix factorization for the postprocessing of low-dose nanoscale characterization data. PCA is found to be the best approach for data denoising. However, the popular scree plot-based method for separation of principal and noise components results in inaccurate or excessively noisy models of the heterogeneous original data, even after Poissonian noise weighting. Manual separation of principal and noise components produces a denoised model which more accurately reproduces physical features present in the raw data while improving SNR by an order of magnitude. However, manual selection is time-consuming and potentially subjective. To suppress these disadvantages, a deep learning-based component classification method is proposed. The neural network model can examine PCA components and automatically classify them with an accuracy of >99% and a rate of ∼2 component/s. Together, multivariate analysis and deep learning enable a deeper analysis of nanoscale materials’ characterization, allowing as much information as possible to be extracted.