This study focuses on the application of fuzzy cognitive diagnostic techniques in the design and optimization of labor education curriculum, and proposes a cognitive diagnostic framework for KSCD by constructing a cognitive model integrating fuzzy cognitive diagnosis with a multilevel scoring Q matrix, combined with a deep neural network approach. The study used empirical analysis to verify the validity of the model, and elementary school students had the highest probability of mastering labor problem solving ability (A7) (0.701±0.294), while labor knowledge (A2) and labor values (A1) were relatively weak (mean values of 0.505 and 0.522, respectively). Labor habits (A6) showed the greatest individual differences (SD=0.319), and the probability of mastering labor emotion regulation strategies (A3) was lower than labor attitudes. Finally, the stratified teaching strategy and dynamic remedial mechanism are proposed to provide theoretical basis and practical path for the precise implementation of labor education.
