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Visible light imaging is becoming an effective tool for high-throughput plant phenotyping and genetic research due to its advantages of rapidity, economy and non-destructiveness. However, the evaluation of yield phenotypic characteristics that are invisible to the naked eye based on visible light images remains to be solved. A technical method for evaluating sorghum aboveground biomass by fusing multi-class features with multi-view images was proposed to address the problem of limited image data accuracy due to overlapping plant leaf occlusion and single variable scale. A two-factor (water and nutrient) and two-level (high and low) experiment was conducted on 300 sorghum plants of 15 germplasm genes. Based on a rotating platform, totally ten side-view images and one top-view image were automatically collected at equal angles for each sorghum plant by using a visible light camera. The morphological characteristics (top-view and side-view projection area), color characteristics (RGB pixel values) and texture characteristics (mean, covariance, homogeneity, etc.) of each sorghum plant were extracted through plant mask images. The information from multiple perspectives was averaged, and 16 color vegetation indices were constructed based on the image R, G, and B pixel values. The results showed that compared with considering image information of a single type of variable and a single perspective, the fusion of morphological, texture and color features based on multi-perspective average image information can significantly increase the ability to obtain the aboveground biomass phenotype of sorghum. The SVR, RF and BPNN algorithms were used to fuse 21 sets of optimized image data variables to construct a regression model for aboveground biomass of sorghum. The RF algorithm model with the highest accuracy had a test set determination coefficient (R2) of 0.881, a root mean square error (RMSE) of 60.714 g/m2, and a mean absolute error (MAE) of 42.364 g/m2. In order to further optimize the parameters of the RF algorithm model, GA, GS and SSA were selected to optimize the hyperparameters of the RF algorithm model. The results showed that the test set R2 of the SSA-RF optimization model was increased to 0.902, the RMSE was 48.706 g/m2, and the MAE was 39.877 g/m2. Based on the fusion of multi-view image morphology, color and texture features, more effective information can be derived from limited information for estimating the aboveground biomass of sorghum, thereby providing a theoretical basis and technical support for sorghum growth monitoring, stress detection, precise application of water and fertilizer, and rapid screening of improved varieties.