Morphology and texture detection, which are important components of tactile sensing, augment the response of human beings to external stimuli. Similarly, tactile sensing-based information acquisition systems in robots can help enhance the interactions of robots with the surroundings. The main drawback of morphology and texture sensing methods is their inability to explain and quantify sensing information, which makes it difficult to utilize prior knowledge and necessitates a new training process to fit the new task, even if the changes between the existing and new tasks are minuscule. Another drawback is its dependence on large datasets. To solve these problems, a hybrid connectionist symbolic model (HCSM) is proposed herein that combines historic symbolic knowledge and end-to-end neural networks. The symbolic model requires a smaller dataset and possesses an improved transferability of detection. Neural networks can be easily established and exhibit better fault tolerance for non-ideal samples. The HCSM model combines these advantages. Experiments with the tactile-based morphology and texture detection demonstrated that the new method can transfer the detection ability to fit new tasks without requiring additional retraining and has a 16% higher recognition precision than a convolutional neural network, LeNet, AlexNet, VGG16, and ResNet. The HCSM method with these features can broaden the range of applications of tactile sensing.