In response to the challenges of quantitative sampling under negative pressure, low real-time detection accuracy of moisture content, and poor stability during the cotton processing, an online moisture content detection device for lint cotton was developed based on the resistance method. A novel online detection method, combining “dynamic quantitative sampling—weight constant pressure measuremen—multi-parameter compensation” was proposed. Key components, including a flip-type sampling mechanism and weight detection system, were designed to achieve stable sampling and continuous detection of lint cotton in a high-speed flow state. Based on the electrical resistance characteristics of lint cotton and environmental temperature and humidity data, a multi-parameter moisture content prediction model was constructed. After comparing various algorithms, the random forest model was identified as the most accurate predictor, with a coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 0.22%. Performance validation tests showed that the average deviation between the proposed online detection device and the standard measurement instrument was 0.1%, with an average detection time of 23.4 s per sample. The impact of sample weight on the moisture content measurement results was weak (Pearson correlation coefficient was 0.47). The experimental results demonstrated that the device offered high detection accuracy and good stability, meeting the real-time and accuracy requirements for moisture content detection in cotton processing. This device can provide reliable technical support for intelligent regulation and quality traceability in the cotton processing industry.