Semi-supervised　learning　suffers　from　the　imbalance　of　labeled　and　unlabeled　training　data　in　the　video　surveillance　scenario.　In　this　paper,　we　propose　a　new　semi-supervised　learning　method　called　SIAVC　for　industrial　accident　video　classification.　Specifically,　we　design　a　video　augmentation　module　called　the　Super　Augmentation　Block　(SAB).　SAB　adds　Gaussian　noise　and　randomly　masks　video　frames　according　to　historical　loss　on　the　unlabeled　data　for　model　optimization.　Then,　we　propose　a　Video　Cross-set　Augmentation　Module　(VCAM)　to　generate　diverse　pseudo-label　samples　from　the　high-confidence　unlabeled　samples,　which　alleviates　the　mismatch　of　sampling　experience　and　provides　high-quality　training　data.　Additionally,　we　construct　a　new　industrial　accident　surveillance　video　dataset　with　frame-level　annotation,　namely　ECA9,　to　evaluate　our　proposed　method.　Compared　with　the　state-of-the-art　semi-supervised　learning　based　methods,　SIAVC　demonstrates　outstanding　video　classification　performance,　achieving　88.76%　and　89.13%　accuracy　on　ECA9　and　Fire　Detection　datasets,　respectively.　The　source　code　and　the　constructed　dataset　ECA9　will　be　released　in　https://github.com/AlchemyEmperor/SIAVC.