Softwarecomponent reusability refers to the development of a new software system using existing software components with or without anymodification. Reusability has a vital role and is becoming a critical concept in the software industry. Various researches have been done on software component reusability estimation.Most studies were concentrated on the six of CK metrics only. Coupling and size software metrics were neglected in the previous studies. Thus, in this work, we considered CK, coupling and size software metrics with effectiveassessment and identification of reusable software components to estimate the reusability levels of object oriented software components.The proposed system has preprocessing and classification phases. In data preprocessing min-max normalization and outlier detection has been done to have the input data similar dimensionality. The proposed software component reusability estimation techniques were consideredseventeen object oriented software metrics using convolutional neural networks. A 3-way softmaxclassifier is used for assigningthe reuse level of a specific class (high, medium and low) reusable software classes.Key software characteristics were considered for proper software component reusability estimation such as coupling, cohesion and complexity metrics. Different performance evaluation criteria like precision, recall and f1-score are used.Fully connected convolutional neural networks used for our work for software component reusability estimation due to its best feature extraction ability and its reduction of network complexity as well as training parameters. The proposed system is implemented using keras (TensorFlow as a backend) in python and tested using object oriented software metrics collected from the comet dataset on the web. We have conducted an experiment on three different java based systems.The model achieved the training accuracy of 95% and for testing accuracy of 95.425% for Eclipse JDT, the training accuracy of 94% and for testing accuracy of 93.803% for Eclipse PDE and the training accuracy of 95% and for testing accuracy of 94.766% for hibernate systems respectively to train the model.