Optimization of science and technology project management system based on hybrid semantic similarity evaluation framework

Abstract

With the increasing complexity of scientific and technological project management, traditional project management systems are faced with the challenges of massive data processing and semantic understanding. The study first discusses the computer-implemented technology of semantic similarity evaluation, analyzes the application of word embedding and deep learning algorithms, and emphasizes the importance of these technologies in natural language processing (NLP). A novel hybrid semantic similarity evaluation framework has been developed and implemented, incorporating an optimized algorithm-based semantic feature extraction module alongside multi-source data fusion and processing techniques modeled on semantic relationships. The framework’s primary objective is to enable precise analysis of multilingual project data by deeply exploring contextual information. Experimental validation highlights the framework’s performance and effectiveness, with particular emphasis on its accuracy in semantic similarity calculations, data processing efficiency, and responsiveness in large-scale environments. During testing, the framework achieved a semantic similarity calculation accuracy of 67.37%, based on a test set comprising 73 samples. An analysis of 74,723 project records revealed that the system’s average response time for multi-source data fusion was 2.3 seconds. Under complex semantic analysis conditions, the framework processed data at a rate of 348.15 items per second. Multiple test results further demonstrated that the error margin in semantic evaluation remained within 2.51 percentage points. These findings confirm that the system optimization successfully meets the anticipated goals in terms of both accuracy and efficiency.