## 1 Introduction
When the marked data sample was insufficient, an OAM mode recognition method based on Conditional Generative Adversarial Networks (CGAN) was proposed to improve the recognition accuracy [21]. A Diffractive Deep Neural Network (D2 NN) was utilized in OAM mode recognition in [22], eliminating the need for a CCD camera to capture images and pass them to a computer, making the communication rate independent of the hardware and neural network computation rate. The above research was dedicated to training and identifying the OAM light distributions captured by CCD cameras; however, some other researchers have performed transformations on the vortex beam before training to highlight the characteristics of different modes. In 2018, Radon transform was introduced to preprocess a light intensity distribution map of an OAM beam to obtain more clearly distinguishing features [23]. A mode recognition technique based on coherent light interference at the receiver side to obtain more obvious recognition features was reported in [24]. A SVM-based single-mode recognition method was proposed in [25], using the relationship between the amount of OAM beam receiving the effect of atmospheric turbulence distortion and the topological charge number as an artificial feature of the design. A joint scheme combining the Gerchberg-Saxton (GS) algorithm and CNN (GS-CNN) to achieve the efficient recognition of the multiplexing LG beams was proposed in [26]. A technique to measure the OAM of light based on the petal interference patterns of modulated vortex beams and an unmodulated incident Gaussian beam reflected by a spatial light modulator was reported in [27]. In light of the aforementioned studies, it may be stated that most research has focused on OAM mode detection by neural networks, CNNs or CNN-based combination methods, preprocessing transform before network training, and OAM mode detection in misaligned or tilt angles special cases. However, in practical applications, there are many different multi-modes superpositions of OAM beams corresponding to quite similar light intensity maps, such as OAM = {\(-2\), \(3\), \(-5\)} and OAM = {\(1\), \(-2\), \(3\), \(-5\)}, OAM= {\(4\), \(-4\)} and OAM= {\(2\), \(-6\)}, etc. Additionally, for a single mode vortex beam with a large topological charge, the number of fringes in the interferogram is large. Because the area of the device that collects the image at the receiving end is certain, it is difficult to determine when the number of interference fringes is large, which will further affect the accuracy of OAM mode recognition. To solve these problems, an OAM mode recognition technique based on an attention pyramid convolutional neural network (AP-CNN) is proposed in this paper. Fine-grained image classification [28] is introduced to make full use of low-level detailed information of a similar intensity in superimposed vortex beams and the dense fringes of a plane wave interfergram of a single-mode vortex beam with a large topological charge. A top-down feature path and a bottom-up attention path structure, combined with an attention pyramid, is adopted to improve the OAM mode recognition accuracy and reduce the bit error rate (BER) for indistinguishable intensity distributions. The remainder of this paper is arranged as follows. The OAM mode recognition technical framework based on AP-CNN is described in Section 2. In Section 3, numerical results and discussions of different transmission conditions are presented to compare the recognition accuracy and bit error ratio. Section 4 is devoted to the conclusion.