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[en] This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers.