Red tide is a common marine ecological disaster caused by the explosive proliferation or high aggregation of certain phytoplankton, protozoa, or bacteria in seawater under specific environmental conditions, leading to discoloration of the water. At present, the main methods for monitoring red tide include fixed-point monitoring of ships, monitoring stations, and aerial and satellite remote sensing monitoring. Among them, satellite remote sensing monitoring is widely used due to its wide monitoring range and comprehensive monitoring information. This article proposes a linear comprehensive identification index (LCI) method for red tide identification in multispectral satellite images based on five sub indices and their comprehensive methods that can indicate red tide outbreaks. Based on the multi day continuous monitoring spectral data of red tide in Xinghai Bay, Dalian City, support vector machine and logistic regression classification were carried out, and the classification plane coefficients were obtained as normalized sub index combination weights. Two optimal combinations of red tide identification indices were obtained. Both methods achieved classification results with an F1 score of 0.86 or higher on the validation set, and the LCI value showed a positive correlation with the probability of red tide occurrence. This article further applies the obtained classification coefficients to the extraction of red tide pixels in multispectral satellite images, and the results show that both linear composite indices have successfully extracted red tide areas in the images. The comprehensive identification index of red tide proposed in this article provides a new spectral index calculation method for red tide identification in remote sensing images.