Entropy is a common description method in the analysis and research of optimization system. To address the lack of in-depth analysis of the inherent relationship between the dynamic behavior and entropy of different optimization systems， an optimization algorithm entropy based on quantum dynamics was proposed. Firstly， based on the similarity between Brownian motion and sampling behavior in physics， a Brownian motion description method for optimization problems was proposed. The mechanical expression of optimization problems was transformed into the form of energy and introduced into the Schr?dinger equation， and an optimization algorithm based on quantum dynamics was proposed. Then， the probability expression of optimization problems under the Schr?dinger equation was combined to obtain optimization algorithm entropy. Finally， the random behavior of particles under constraint of the objective function was analyzed， and the relationship between basic search behavior of optimization systems under quantum dynamics and entropy was given. By tracking and analyzing the dynamic behavior and entropy change trend of optimization systems from three different aspects： reference energy， free particle kinetic energy， and objective function disturbance， the correlation between entropy and search behavior of optimization systems was verified through experiments. Experiments results show that optimization algorithm entropy based on quantum dynamics can deeply analyze optimization process， providing a new idea and method for studying optimization algorithms.

Random sequence as the basis for information security, the quality depends on its use of random source, how to get high-quality random source research in the field of information security is one of the difficulties. Portrait facial features through the use of the difference and get (shoot) random process, a new image as a random source of portraits of random sequence generation methods; through the image acquisition process and the human biological characteristics of random noise a combination of random source. Simulation tests show that this method does not have a random sequence generated by linear correlation and nonlinear correlation, with excellent uniformity and FIPS PUB 140-2 and NIST 800-22 test pass rates to meet the needs of information security, and the method is simple and easy to implement.

During the process of enterprise investment attraction， there are multi-dimensional risks. Traditional risk assessment methods are difficult to identify these risks timely and accurately due to information distortion and complex relationships in economic behaviors. To address the above issues， a risk analysis framework integrating Large Language Model （LLM） and Graph Neural Network （GNN） was proposed. The semantic understanding capability of LLM was utilized to assist the GNN in constructing a more comprehensive and accurate dynamic heterogeneous knowledge graph of enterprises， thereby solving the information distortion problem caused by static data. On this basis， to address the shortcomings of GNN in terms of deep and semantic expression abilities， a knowledge-based semantic structure mining module was designed， and Qwen large model was combined to enhance the semantic accuracy of node representations. Furthermore， an Integrated One Graph （IOG） module was proposed to unify node classification and graph classification tasks into the prediction of “focus nodes”. Through a unified prediction mechanism， predictions for different graph structure types were achieved， thereby improving the model’s generalization ability on different datasets significantly. The IOG-CIQAN（In One Graph with Collective Intelligence and Qwen2 Assistance Network） model constructed on the basis of this framework achieved accuracy over 87% on all of three risk analysis datasets in labor， finance， and administration compared to multiple baseline models such as Capsule Network （CapsNet）.