The variability of phytoplankton distribution has been unraveled by high-frequency measurements. Such a resolution can be approached by automated pulse-shape recording flow cytometry (AFCM) operating at hourly sampling resolution. AFCM records morphological and physiological traits as single-cell optical pulse shapes that can be used to classify cells into phytoplankton functional groups (PFGs). However, the associated manual post-processing of the data coupled with the increasing size and number of datasets is time-consuming and error-prone. Machine learning models are increasingly used to run automatic classification. Yet, most of the existing methods either present a long training process, need to manually design features from the raw optical pulse shapes, or are dedicated to images only. In this study, we present a convolutional neural network (CNN) to classify several PFGs using AFCM pulse shapes. The uncertainties of manual classification were first estimated by comparing experts' recognition of six PFGs. Consensual particles from the manual PFG classification were used to train and validate the CNN. The CNN obtained competitive performances compared to other models used in the literature and remained robust across several sampling areas, and instrumental hardware and settings. Finally, we assessed the ability of this classifier to predict phytoplankton counts at a Mediterranean coastal station and from a cruise in the South-West Indian Ocean, providing a comparison with the manual classification over 3-month periods and a 2h frequency. These promising results strengthen the near real-time observation of PFGs, especially required with the increasing use of AFCM in monitoring research programs.