Considering the defects of the current abnormal monitoring methods for symbiosis monitoring data of ecological environments, a method for abnormal monitoring modeling of symbiosis monitoring data of ecological environments based on density clustering is proposed. A hybrid algorithm of self-adaptive matrix estimation and random gradient descent is introduced to filter out the dirty data. Genetic optimization is used to estimate the parameters of incomplete monitoring data and obtain the optimal data parameters. Based on the optimal parameters, Markov chain and Monte Carlo algorithm are used to estimate and fill the missing data. The symbiosis monitoring data set of an ecological environment is divided into extreme cluster, wild value cluster, and normal cluster. The abnormal possibility is given in different ways in each cluster, and the time sequence diagram of abnormal possibility considering independent variables and effect quantities is obtained. On this basis, the improved local abnormal coefficient algorithm is used to set up the abnormal monitoring model of symbiosis monitoring data of the ecological environment and complete the abnormal monitoring. The experimental results imply that the method in this paper has high monitoring accuracy, high monitoring efficiency, high detection rate, and low false detection rate. The proposed method improves the convergence speed and effectiveness of data cleaning and improves the estimation accuracy of missing data. Therefore, it can achieve the purpose of optimizing the abnormal monitoring effect of the ecological environment symbiosis monitoring data.