Against the backdrop of the continuous advancement of registration-based IPO reform and innovation-driven development strategies

innovative SMEs have developed rapidly in the capital market. However

they also face problems such as unstable profit models

high financing pressure

weak governance structures

and unbalanced information disclosure

making them a key group where the risks of financial fraud and market manipulation are more likely to accumulate. Addressing the gap in existing research that rarely focuses simultaneously on the "financial fraud—market manipulation—investor protection" risk chain

this paper takes innovative small and medium-sized enterprises as its research object and proposes an intelligent early warning research framework based on multi-source data fusion. The paper constructs a risk identification index system from four dimensions: financial

governance

trading

and text/sentiment

and proposes a design for early warning models that uses Logistic regression as a benchmark while incorporating random forest

XGBoost

LightGBM

SVM

and NLP methods. Additionally

this paper emphasizes that early warning results should be further transformed into risk scores

risk ratings

and explicable warning reasons to enhance their understandability and usability in investor protection practices. This article aims to provide a clear analytical framework for intelligent early warning research on financial fraud in innovative small and medium-sized enterprises.