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Particle dynamics characterization is fundamental for understanding the biophysical laws orchestrating cellular processes. To classify the dynamic behaviors governing biological particles, we develop a neural network model built on geometric descriptors of trajectories. The model infers the stochastic laws governing the trajectory, enabling the detection of a large family of dynamic behaviors, especially within the subdiffusive regime that characterizes cell signaling processes. Finally, we propose a framework to robustly detect dynamic changes in composed trajectories based on the variability of prediction scores on successive sub-trajectories. The method is validated on simulated composed trajectories simulating the activation pathway of receptors CCR5.