Aiming at problem of inaccurate predicting of traditional fuzzy C-means（ FCM） clustering algorithm in coal and gas outburst nonlinear systems high-dimensional multivariate. A new hybrid approach includes an artificial fish swarm algorithm（ AFSA） and FCM clustering algorithm based on principal component analysis screening（ AFSA-PCA-FCM） is proposed. The approach uses AFSA to optimize initial parameters of FCM

and update the optimal solution of AFSA in bulletin board

so as to determine cluster center

through principal component analysis（ PCA） algorithm find a best fit of cluster centers

and through wireless sensor networks（ WSNs）

real-time collect data of the main factors affecting underground coal and gas

the data are used as sample

and the preprocessed data are analyzed

modeling

and then compare the results with AFSA-BP

FCM and other methods. The evaluation results show that

for the prediction of coal and gas outburst strength

this method has higher accuracy

rapidity

and can stably converge to globally optimal solution.