The content of elongated and flat aggregates is a critical detection index in road engineering. In this study, using image sequences of falling aggregates, two multi-view morphological methods were proposed for aggregate shape classification. The multi-view features of the aggregates were constructed by combining multiple single-view features from different views. Furthermore, a joint-view normalization method was proposed to improve the three-dimensional shape characterization capability of the multi-view features. Based on the multi-view features, a support vector machine was adopted to achieve aggregate shape classification. In addition, multiple two-dimensional aggregate morphologies from different views were directionally guided and fused to construct a multi-view image, which could reflect the three-dimensional shape of the aggregates. The multi-view images were then fed into ResNet18 for aggregate-shape classification. Notably, compared with single-view morphological methods based on the aggregate morphology in the vertical and random views, the two multi-view morphological methods developed herein significantly improved the classification accuracy. Moreover, it was found experimentally that increasing the height of the field of view improved the classification accuracy of the multi-view morphological methods. The proposed multi-view morphological methods demonstrate significant potential for application in the automatic identification of elongated and flat aggregates in road construction.