Flow cytometry is a high-throughput detection technique widely used in life science research and clinical diagnostics. However, conventional flow cytometers exhibit suboptimal performance when handling complex data dimensions and severe noise interference. To enhance the capability of flow cytometry in processing multi-parameter, high-dimensional data while ensuring timeliness and accuracy, this study proposes an intelligent flow cytometry analysis system. The system encompasses hardware design, software architecture, and algorithmic frameworks for flow cytometry. At the hardware level, a real-time data acquisition system was developed based on the cooperative operation of FPGA and ARM. On the software side, an embedded system with a Linux-based architecture was constructed, incorporating a suite of preprocessing, parsing, and batch normalization methods. For intelligent flow cytometry data analysis, a self-organizing mapping algorithm was introduced for dimensionality reduction, combined with an improved residual network from the field of deep learning, resulting in the development of an SE-ResNet-50 deep convolutional neural network model. Experimental results demonstrate that the SE-ResNet-50 model achieves a 4% improvement in overall accuracy and a 3.8% increase in precision compared to the original ResNet-50. The collaborative workflow integrating SOM and SE-ResNet-50 effectively processes the vast amounts of high-dimensional data acquired by flow cytometry. The findings validate the superiority of the proposed approach.