Single-phase auto-reclosing schemes depend on two main characteristics: (i) secondary arc extinction instant (ii) fault type (temporary or permanent). However, secondary arc quenching time greatly varies from no-arc to prolonged-arc generation depending on the secondary arc current and meteorological factors (wind, thermal buoyancy, etc.). Existing solutions face challenges in making reclosing decisions owing to variable secondary arc quenching instant, non-linear arc voltage characteristics, and varying recovery voltage waveform magnitude. To comprehensively address the discussed problems, an approach based on a particle filter algorithm along with a convolutional neural network is developed. Initially, harmonics of compromised phase are online estimated using a particle filter algorithm, and two indices are developed: (i) estimation of the precise moment of secondary arc extinction (ii) fault nature. Then, extensive simulations are carried out with variable arc resistance, line lengths, no-shunt, and shunt configurations to develop a database based on these two indices. Finally, support vector machine, fully connected deep neural network, and the convolutional neural network are applied to the dataset to predict the reclosing decisions. The comparative study of classifiers confirms the effectiveness of the proposed convolutional neural network in terms of overall model and inter-class accuracy under a noisy and noise-free environment.