At　present,　the　research　on　photovoltaic　companies＇　financial　risk　early　warning　model　mainly　focuses　on　financial　indicators　and　non-financial　indicators　from　corporate　governance　structure　and　external　audit　opinions.　There　are　few　literature　studies　on　the　companies＇　internal　information　from　their　annual　report.　To　solve　the　above　problem,　firstly,　this　paper　aims　to　establish　a　comprehensive　assessment　indicators　system　including　financial　and　non-financial　indicators　considering　the　companies＇　internal　information.　Secondly,　this　paper　uses　text　mining　and　a　binary　logistic　regression　model　to　evaluate　the　financial　risk　for　37　listed　companies　in　the　photovoltaic　industry.　The　results　showed　that　profitability　was　the　most　significant　factor.　Probability,　as　well　as　negative　sentiment　ratios,　are　both　negatively　correlated　with　the　occurrence　of　financial　risk,　while　development　capability　is　positively　associated　with　financial　risk.　These　findings　can　be　used　as　an　effective　supplement　for　financial　risk　evaluation　in　the　photovoltaic　industry　and　provide　reference　strategies　for　developing　listed　companies　in　the　photovoltaic　industry.