Analysis of intermittent dynamics from experimental data is essential to promote the
understanding of practical complex nonlinear systems and their underlying physical
mechanisms. In this paper, reservoir computing enabled dynamics prediction and identification
of two types of intermittent switching using experimental data from discrete-mode
semiconductor lasers are rigorously studied and demonstrated. The results show that, for the
dynamics prediction task, both regular and irregular intermittent switching can be predicted
reliably by reservoir computing, achieving the average normalized mean-square error of less
than 0.015. Additionally, the impact of the number of virtual nodes in the reservoir layer, as
well as the train-test split ratio on prediction performance, is explored. For the dynamic
identification task, a 2-class classification test is adopted, and the corresponding binary
accuracy is calculated to evaluate the identification performance. The results demonstrate that,
the accuracy of identifying both regular and irregular intermittent switching exceeds 0.996.
Compared with the conventional amplitude threshold identification method, the reservoir
computing-driven dynamics identification method exhibits superior accuracy, especially in the
intermittent transient transition regions