<p>Ordinal symbolic analysis, an information encoding scheme, is commonly employed to investigate dynamic properties of nonlinear systems. The encoded information changes with alterations in neural network dynamics, thereby reflecting the functional states of the neural network. Our study uses a neural network connected by a small-world model to explore the influence of temperature changes on the probability of epileptic seizures. By adopting the coding method of ordinal symbol analysis, it is found that this coding method can well correspond to the influence of temperature rise on epileptic seizures. At the same time, the changes in the ordinal symbol patterns under the condition of electrical stimulation and the rules for parameter selection are studied. It is found that the effective range of the positive rectangular square-wave electrical stimulation conforms to the parameter range of deep brain stimulation; meanwhile, the parameter value rules of the positive cosine electrical stimulation for the treatment of epileptic seizures are obtained. Systematic modulation of temperature and electrical stimulation parameters revealed synchronization dynamics governing neural network activity. From this, it can be concluded that theoretically, we can use the ordinal symbol coding method to quantitatively represent the different degrees of epileptic seizures in clinical diagnosis, which has certain theoretical guiding significance for the diagnosis and treatment of epilepsy in clinical medicine.</p>