Although the grasping state analysis is vital in the study of manipulators, the grasping state analysis of soft manipulators as an independent research topic is not much so far. This paper proposes a novel pneumatic soft manipulator with a flexible tactile sensor array (SM-FTSA). The flexible tactile sensor array comprises piezoresistive materials with a porous structure. An equal potential approach is adopted to realize the collection of tactile signals of the SM-FTSA. Inspired by the grasping analysis of rigid manipulators, we propose 4 grasping states for the SM-FTSA, including inflating, shaking, stable, and slipping. Based on the experimental data, we conduct grasping experiments on 12 objects with SM-FTSA, and we propose 10 features that reflect the grasping state. Several machine learning methods are utilized to classify the grasping state. Among them, the Random Forest method presents the best performance, and the average classification accuracy reaches 99%.