Enterprise scientific and technological achievements contain complex data and cover a large amount of sensitive information. The existing text classification results cannot meet the actual confidentiality management needs, potentially leading to risks of data leakage or unauthorized access. To address this, an enterprise scientific and technological achievement management platform based on natural language processing is designed to solve the problem of classic keyword retrieval being unable to accurately classify confidential documents. The platform uses Convolutional Neural Networks(CNN) to automatically extract text features, with Support Vector Machine(SVM) as the final classifier, developing a CNN-SVM model. It uses multiple convolution kernels of different dimensions for convolution operations, utilizes fully connected layers to receive and process output data from the attention layers, and applies SVM classifiers to classify scientific and technological achievement texts. The attachment management module deploys Swift Object Storage Service(Swift). Finally, the encryption processing of scientific and technological achievement text data during transmission and storage is implemented through the Advanced Encryption Standard(AES) encryption algorithm, thus achieving the design of the enterprise scientific and technological achievement management platform. In order to verify the effectiveness of the design platform, a comparative experiment was conducted with System A and System B. The experiment shows that under data theft attacks of varying frequencies, the amount of stolen scientific and technological achievement data does not exceed 1 MB, the retrieval consistency can reach over 90%, and the recall rate for semantic confidentiality inspections after document classification can reach up to 97%. This indicates that the automatic document classification effect of the designed platform presented in this paper is good and can play a role in protecting enterprise intellectual property rights. The enterprise scientific and technological achievement management platform designed in this study effectively improves the confidentiality management level of technology achievement documents by combining NLP technology and advanced encryption methods. It can prevent data leakage and illegal access to a large extent, while ensuring the accuracy and effectiveness of document classification.